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328 results on '"A. Bourennane"'

1. Multiscale denoising autoencoder for improvement of target detection

Author

Xuefeng Liu, Qiaoqiao Sun, Bin Liu, Salah Bourennane, Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Denoising autoencoder, 010504 meteorology & atmospheric sciences, Computer science, business.industry, 0211 other engineering and technologies, Hyperspectral imaging, Pattern recognition, 02 engineering and technology, 01 natural sciences, Noise, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], General Earth and Planetary Sciences, Artificial intelligence, business, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences
Abstract

Target detection is one of the most important applications of hyperspectral technology. However, due to spectral variations caused by noise or environment, the within-class variation is enlarged wh...

Published
2021
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2. Multi-size and multi-model framework based on progressive growing and transfer learning for small target feature extraction and classification

Author

Xuefeng Liu, Salah Bourennane, Qiaoqiao Sun, Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, China, and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
010504 meteorology & atmospheric sciences, Computer science, business.industry, Deep learning, Feature extraction, 0211 other engineering and technologies, Hyperspectral imaging, Pattern recognition, 02 engineering and technology, Small target, 01 natural sciences, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], General Earth and Planetary Sciences, Artificial intelligence, Transfer of learning, business, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences
Abstract

Many deep learning-based models have been designed to extract features for hyperspectral classification. The high-dimensional target data in hyperspectral images (HSIs) can be mapped to correspondi...

Published
2021
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3. Retinal vessel segmentation using a probabilistic tracking method

Author

Mouloud Adel, Salah Bourennane, Yi Yin, GSM (GSM), Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Receiver operating characteristic, business.industry, Probabilistic logic, 02 engineering and technology, Geometric shape, Edge detection, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Artificial Intelligence, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Signal Processing, 0202 electrical engineering, electronic engineering, information engineering, Maximum a posteriori estimation, 020201 artificial intelligence & image processing, Segmentation, Computer vision, Computer Vision and Pattern Recognition, Noise (video), Artificial intelligence, False positive rate, business, Software, ComputingMilieux_MISCELLANEOUS, Mathematics
Abstract

International audience; Vessel structures such as retinal vasculature are important features for computer-aided diagnosis. In this paper, a probabilistic tracking method is proposed to detect blood vessels in retinal images. During the tracking process, vessel edge points are detected iteratively using local grey level statistics and vessel's continuity properties. At a given step, a statistic sampling scheme is adopted to select a number of vessel edge points candidates in a local studying area. Local vessel's sectional intensity profiles are estimated by a Gaussian shaped curve. A Bayesian method with the Maximum a posteriori (MAP) probability criterion is then used to identify local vessel's structure and find out the edge points from these candidates. Evaluation is performed on both simulated vascular and real retinal images. Different geometric shapes and noise levels are used for computer simulated images, whereas real retinal images from the REVIEW database are tested. Evaluation performance is done using the Segmentation Matching Factor (SMF) as a quality parameter. Our approach performed better when comparing it with Sun's and Chaudhuri's methods. ROC curves are also plotted, showing effective detection of retinal blood vessels (true positive rate) with less false detection (false positive rate) than Sun's method

Published
2021
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4. Video steganalysis in the transform domain based on morphological structure of the motion vector maps

Author

Ahmed Bouridane, Abdellatif Zouak, Ismahane Cheheb, Yves Michels, Salah Bourennane, Beghdadi, A, Alaya Cheikh, F, Tavares, JMRS, Mokraoui, A, Valenzise, G, Oudre, L, Qureshi, MA, University of Northumbria at Newcastle [United Kingdom], Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Steganalysis, Cover (telecommunications), Steganography, business.industry, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, Object (computer science), Motion vector, Motion (physics), Visualization, Domain (software engineering), ComputingMethodologies_PATTERNRECOGNITION, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Artificial intelligence, business, ComputingMilieux_MISCELLANEOUS
Abstract

Steganography is the art of transmitting hidden messages through a cover object without raising any suspicion. In contrast, steganalysis is the science of detecting the presence of hidden information and a significant amount of research has been focused on multimedia steganalysis. In this paper, a video steganalysis method is proposed to detect the presence of hidden data by analysing the structure of the motion vectors in the compressed video data. The proposed method is based on the classification of features extracted from the morphology of the motion vector map. The proposed method has been evaluated on a large dataset of short videos with variable resolution and quality and the results suggest the effectiveness of the proposed modelling scheme.

Published
2021
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5. Efficient Diagnosis COVID-19 using Gabor and Transfer Learning

Author

Mebarka Belahcene, Meryem Ketfi, Salah Bourennane, Institut FRESNEL (FRESNEL), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)

Subjects
2019-20 coronavirus outbreak, Coronavirus disease 2019 (COVID-19), Computer science, business.industry, Deep learning, Feature extraction, Gabor wavelet, Chest ct, Image processing, Pattern recognition, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Artificial intelligence, Transfer of learning, business, ComputingMilieux_MISCELLANEOUS
Abstract

In this work, we propose a Deep Learning (DL) based approach to detect COVID-19 infection from chest CT Scans and X-rays images. The Keras-Tensorflow architecture is used with VGG16. Gabor Wavelet (GW) is used for feature extraction. The VGG16 Transfer Learning (TL) was used in the classification COVID-19 and no-COVID-19. Experiments are valued on three publicly available datasets. Our results show that the proposed approach achieves the very high performance of the VGG16 without Gabor on COVID19-CT dataset contains 3000 positive CT scans with clinical findings of COVID-19, and 3000 negative images without findings of COVID-19 with performance accuracy for the two cases X-rays et CT. It should be noted also that system using Gabor has a lot of performance regarding the speed of image processing time.

Published
2021
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6. Target Detection of Hyperspectral Image Based on Faster R-CNN with Data Set Adjustment and Parameter Turning

Author

Hao Wang, Qiaoqiao Sun, Xuefeng Liu, Yuping Feng, Min Fu, Salah Bourennane, Li Ma, Congcong Wang, GSM (GSM), Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), and Bourennane, Salah

Subjects
[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, business.industry, Computer science, Deep learning, Feature extraction, 0211 other engineering and technologies, Hyperspectral imaging, Pattern recognition, Image processing, 02 engineering and technology, Convolutional neural network, Object detection, Field (computer science), Data set, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Artificial intelligence, business, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering
Abstract

Deep learning target detection based on faster regions with convolutional neural network (Faster R-CNN) features has been applied in image processing successfully, however, it is rarely introduced to the field of hyperspectral image (HSI) target detection due to the tensor characteristics and the lack of training samples of HSI data. In this paper, the target detection based on Faster R-CNN is proposed to HSI with data set adjustment and parameter turning. As a typical tensor data, HSIs contain two-dimensional (2-D) spatial information and one dimensional (1-D) spectral information. It contains more information than ordinary images, and has unique advantages in the field of ground object and sea target detection. Therefore, the original HSI is firstly adjusted to the data set format required by the model, and the final Faster R-CNN sample data set can be achieved by combining the data set of Google Earth images. Next, a Faster R-CNN network suitable for HSI data could be built. Finally, to improve the accuracy of target detection, some parameters of Faster R-CNN would be tuned. The numerical results show that the method has the potential advantages of high precision and high speed in HSI target detection, and will have broad application prospects.

Published
2019
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7. Data ScienceTheory, Algorithms, and Applications

Author

K., Gyanendra, Soni, Badal, Bourennane, Salah, Brandao-Ramos, Alexandre Carlos, Department of Computer EngineeringNational Institute of Technology KurukshetraKurukshetraIndia, Department of Computer Science and EngineeringNational Institute of Technology SilcharSilcharIndia, Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Mathematics and Computing InstituteUniversidade Federal de ItajubaItajubaBrazil, and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2021

8. Wireless NoC for Inter-FPGA Communication: Theoretical Case for Future Datacenters

Author

El-Bay Bourennane, Qaiser Ijaz, Imagerie et Vision Artificielle [Dijon] (ImViA), Université de Bourgogne (UB), Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, and HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS)

Subjects
020203 distributed computing, Computer science, business.industry, Wireless network, Distributed computing, Cloud computing, 02 engineering and technology, Virtualization, computer.software_genre, Bottleneck, 020202 computer hardware & architecture, Software deployment, 0202 electrical engineering, electronic engineering, information engineering, Wireless, [INFO]Computer Science [cs], business, Field-programmable gate array, computer, ComputingMilieux_MISCELLANEOUS, Efficient energy use
Abstract

Integration of FPGAs in datacenters might have different motivations from acceleration to energy efficiency, but the goal of better performance tops all. FPGAs are being utilized in a variety of ways today, tightly coupled with heterogenous computing resources, and as a standalone network of homogenous resources. Open source software stacks, propriety tool chain, and programming languages with advanced methodologies are hitting hard on the programmability wall of the FPGAs. The deployment of FPGAs in datacenters will neither be sustainable nor economical, without realizing the multi-tenancy in multiple FPGAs. Inter-FPGA communication among multiple FPGAs remained relatively less addressed problem this area. In this proposed study, we made a theoretical case as a potential solution for this bottleneck problem to realize multi-tenant multi-FPGAs in datacenters. The proposed scheme poses a challenge as well as an opportunity for the scientific community to contribute.

Published
2020
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9. A Tensor Approach for Activity Recognition and Fall Detection Using Wearable Inertial Sensors

Author

Hakim Tayakout, Salah Bourennane, Oussama Kerdjidj, Khalida Ghanem, Elhocine Boutellaa, Farid Harizi, Institut FRESNEL (FRESNEL), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)

Subjects
Multilinear map, Computer science, business.industry, 020206 networking & telecommunications, Pattern recognition, 02 engineering and technology, Accelerometer, External Data Representation, Data structure, Activity recognition, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Principal component analysis, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Tensor, Artificial intelligence, business, Representation (mathematics), ComputingMilieux_MISCELLANEOUS
Abstract

Tensors are high order muti-way arrays which extend vectors and matrices to higher, greater than three, dimensions. Rather than vectorizing data, tensors allow its representation in its original raw form, hence offering better understanding and interpretation of the underlying data structure. In this paper, we propose an activity recognition and fall detection approach which makes use of tensors as the mean for wearable inertial sensors data representation. To reduce the tensor size and only keep important information, we project the tonsorial data into a lower dimensional space using multilinear principle component analysis. To evaluate the proposed approach, we carry out experiments on a publicly available large fall and activities dataset. The results show the defectiveness of tensorizing data compared to its processing through concatenated vectors.

Published
2020
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10. High speed EIT with multifrequency excitation using FPGA and response analysis using FDM

Author

Darnajou, Mathieu, Dupre, Antoine, Dang, Chunhui, Ricciardi, Guillaume, Bourennane, Salah, Bellis, Cedric, Mylvaganam, Saba, Laboratoire d'essais de Thermohydraulique et d'Hydromécanique analytique du Coeur et des circuits (LTHC), Service de Technologie des Composants et des Procédés (STCP), Département Technologie Nucléaire (DTN), CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Département Technologie Nucléaire (DTN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institut FRESNEL (FRESNEL), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Mécanique et d'Acoustique [Marseille] (LMA ), University of South-Eastern Norway (USN), MISTRAL-Lab, and Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)

Subjects
two-phase flow, Electrical impedance tomography, [SPI.MECA.STRU]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Structural mechanics [physics.class-ph], [SPI.MECA.VIBR]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Vibrations [physics.class-ph], ONE-SHOT, real-time, [PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex], ComputingMilieux_MISCELLANEOUS, FPGA, frequency division multiplexing, [SPI.MECA.MEFL]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Fluids mechanics [physics.class-ph]
Abstract

International audience; The investigation of quickly-evolving flow patterns in high-pressure and high-temperature flow rigs is crucial due to inherent hazards. There is a dire need for a high-speed, non-intrusive imaging technique to identifycharacteristic flow phenomena to alleviate these hazards. Electrical Impedance Tomography (EIT) enables reconstruction of the admittivity distribution of the flowing medium(s), facilitating the characterisation of its/their flow. The requirement for images at high frame-rates led to simultaneous voltage excitations over electrodes on the periphery of the flow associated to Frequency Division Multiplexing (FDM), doped ONe Excitation for Simultaneous High-speed Operation Tomography (ONE-SHOT) method. In previous studies, we have demonstrated the possibility of the full implementation of simultaneous excitations and measurements strategies for EIT, which maximise the number of measurements for 16 electrodes. This article presents the details of the proposed method and the signal generation/acquisitionfirmware basedon a Field Programmable Gate Array (FPGA) data acquisition system focusing on hardware and software integration and the signal processing involved in realising the ONE-SHOT operation of the EIT. It is shown that the simultaneously generated signals are successfully discriminated and used for image reconstruction, at a rate up to 3906 frames per second (fps). The associated signal-to-noise ratio varies in the 55.6 dB–69.1 dB range, depending on the generated frequency in the range of 3.906 kHz–468.7 kHz.

Published
2020
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11. Hyperspectral image classification with unsupervised feature extraction

Author

Salah Bourennane, Qiaoqiao Sun, GSM (GSM), Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
010504 meteorology & atmospheric sciences, Computer science, business.industry, Deep learning, Feature extraction, 0211 other engineering and technologies, Hyperspectral imaging, Pattern recognition, 02 engineering and technology, 01 natural sciences, [SPI]Engineering Sciences [physics], Earth and Planetary Sciences (miscellaneous), Hyperspectral image classification, Artificial intelligence, Electrical and Electronic Engineering, business, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences
Abstract

Deep learning models have been widely applied to extract the high-level features of hyperspectral images (HSIs) due to their strong data mining ability, and some encouraging results have been obtai...

Published
2020
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12. Digital soil mapping for sustainable management

Author

Dominique Arrouays, Zamir Libohova, Budiman Minasny, Vera Leatitia Mulder, Laura Poggio, Pierre Roudier, Hocine Bourennane, Philippe Lagacherie, Guillaume Martelet, Manuel Martin, Pierre Nehlig, Richer-De-Forges, Anne C., InfoSol (InfoSol), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), USDA-ARS : Agricultural Research Service, University of Sydney, Soil Geography and Landscape Group, Wageningen University and Research [Wageningen] (WUR), World Soil Information (ISRIC), Manaaki Whenua – Landcare Research [Lincoln], Unité de Science du Sol (Orléans) (URSols), Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Bureau de Recherches Géologiques et Minières (BRGM) (BRGM), and Le Studium - Loire Valley Institute for Advanced Studies. FRA.

Subjects
[SDV]Life Sciences [q-bio], ComputingMilieux_MISCELLANEOUS
Abstract

National audience

Published
2020

13. Target detection based on classification in shadow region of hyperspectral image

Author

Liu, Xuefeng, Wang, Congcong, Fu, Min, Bourennane, Salah, GSM (GSM), Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Bourennane, Salah, Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

14. Optimal Parameter Selection in Hyperspectral Classification Based on Convolutional Neural Network

Author

Qiaoqiao Sun, Xuefeng Liu, Salah Bourennane, Bourennane, Salah, GSM (GSM), Institut FRESNEL (FRESNEL), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), and Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)

Subjects
Computer science, Estimation theory, business.industry, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Deep learning, Computer Science::Neural and Evolutionary Computation, 0211 other engineering and technologies, Hyperspectral imaging, 020206 networking & telecommunications, Pattern recognition, 02 engineering and technology, Convolutional neural network, Image (mathematics), Variable (computer science), [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 0202 electrical engineering, electronic engineering, information engineering, Key (cryptography), Network performance, Artificial intelligence, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], business, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering
Abstract

Classification is a key technique in hyperspectral image (HSI) applications. Deep learning algorithms, which exhibit strong modeling and representational capabilities, have been successfully adopted in fields such as image and language processing. And convolutional neural networks (CNNs) have been used for HSI classification and some interesting results have been obtained. Owing to local connection and weight sharing, the number of parameters is reduced to some extent, but there are still many parameters and the deeper the network, the larger is the number of parameters. The network performance is strongly influenced by the parameter settings. To obtain the optimal CNN parameters for HSI classification, this paper proposes a classification method based on a CNN with parameter tuning (CNN-PT). The network parameters are tuned in turn according to the unique variable principle. Simulation results show that the proposed CNN-PT method has considerable potential for HSI classification compared to previous methods.

Published
2019

15. Unsupervised feature extraction based on improved Wasserstein generative adversarial network for hyperspectral classification

Author

Salah Bourennane, Qiaoqiao Sun, Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), and Bourennane, Salah

Subjects
Discriminator, Computer science, business.industry, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Dimensionality reduction, Deep learning, Feature extraction, 0211 other engineering and technologies, Hyperspectral imaging, Pattern recognition, 02 engineering and technology, Convolutional neural network, ComputingMethodologies_PATTERNRECOGNITION, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Principal component analysis, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], business, Classifier (UML), ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering
Abstract

Accurate classification is one of the most important prerequisites for hyperspectral applications and feature extraction is the key step of classification. Recently, deep learning models have been successfully used to extract the spectral-spatial features in hyperspectral images (HSIs). However, most deep learning-based classification methods are supervised and require sufficient samples to guarantee their performance. And the labeled samples in HSI are limited. To solve this problem, unsupervised feature extraction based on improved Wasserstein generative adversarial network (WGAN) is proposed in this paper. Further, in order to fully explore the spectral-spatial features in HSIs, a three-dimensional (3D) model is designed. Considering that its difficult for GANs to generate high dimension data, the dimension of HSI is reduced firstly by principle component analysis (PCA). Then, an improved WGAN is trained unsupervised with HSIs after dimensionality reduction to achieve the stable status. Finally, the discriminator of the trained improved WGAN is used as a feature extractor and followed by a classifier, which can be used to classify the HSIs. In order to evaluate the performance of the proposed method, PCA-based methods and GAN-based methods are compared in the experiment of a real-world HSI. Experimental results have shown that our proposed method gained more promising results which has great potential prospects in HSI classification.

Published
2019

16. Hyperspectral image segmentation based on the estimation of the multiway signal subspace dimension

Author

Delmas, Pierre, Fossati, Caroline, Bourennane, Salah, GSM (GSM), Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), and Bourennane, Salah

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

17. Target Detection Based on Spectral Derivation in HSI Shadow Region Classified by Convolutional Neural Networks

Author

Min Fu, Yue Meng, Hao Wang, Xuefeng Liu, Congcong Wang, Salah Bourennane, Institut FRESNEL (FRESNEL), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)

Subjects
010504 meteorology & atmospheric sciences, business.industry, Computer science, 0211 other engineering and technologies, Hyperspectral imaging, Pattern recognition, 02 engineering and technology, 01 natural sciences, Convolutional neural network, Image (mathematics), Reflection (mathematics), [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Shadow, General Earth and Planetary Sciences, Artificial intelligence, business, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences
Abstract

Because of the low reflection value of the shadow regions in hyperspectral image (HSI), these regions were deleted directly or ignored in target detection or classification. There are some studies ...

Published
2019
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18. Le Réservoir Utilisable : Evaluation - Concepts - Incertitudes

Author

Isabelle Cousin, Julie Constantin, Robin Lalu, Jacques-Eric Bergez, Bernard Lacroix, Claude Doussan, Mercedes Roman Dobarco, Maud Seger, Mohamed Alkacem, Guillaume Coulouma, Christine Le Bas, Philippe Lagacherie, Samuel Buis, Victor Picheny, Vincent Rivalland, Luc Champolivier, Hocine Bourennane, Martine Guerif, Unité de Science du Sol (Orléans) (URSols), Institut National de la Recherche Agronomique (INRA), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT), ARVALIS - Institut du végétal [Paris], Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), InfoSol (InfoSol), Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Unité de recherche Science du Sol (USS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
stockage d'eau, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, réserve en eau du sol, eau du sol, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, teneur en eau, réserve utile, Sciences de la Terre, Agricultural sciences, Earth Sciences, réservoir utilisable, propriété du sol, Sciences agricoles, ComputingMilieux_MISCELLANEOUS
Abstract

National audience

Published
2019

19. Monolithic asymmetric switching cells integrated on vertical multi-terminal Si power chips

Author

Frédéric Richardeau, Adem Lale, A. Bourennane, Équipe Intégration de Systèmes de Gestion de l'Énergie (LAAS-ISGE), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J), Convertisseurs Statiques (CS), LAboratoire PLasma et Conversion d'Energie (LAPLACE), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique (CNRS), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Convertisseurs Statiques (LAPLACE-CS), IRT Saint Exupéry - Institut de Recherche Technologique, Richardeau, Frédéric, Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), and Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP)

Subjects
Power management, business.industry, Computer science, 020209 energy, 020208 electrical & electronic engineering, [SPI.NRJ]Engineering Sciences [physics]/Electric power, Electrical engineering, 02 engineering and technology, Insulated-gate bipolar transistor, Decoupling capacitor, 7. Clean energy, Manufacturing cost, law.invention, Capacitor, Reliability (semiconductor), law, Power module, 0202 electrical engineering, electronic engineering, information engineering, Power semiconductor device, business, ComputingMilieux_MISCELLANEOUS, [SPI.NRJ] Engineering Sciences [physics]/Electric power
Abstract

International audience; Power electronic converters (power modules) are essential devices for high efficient power management. Applications are ever more demanding in terms of compactness, reliability and cost reduction with increased intrinsic performance. The current technology of standard power modules, that use IGBT chips, is reaching its limits and the margins for improvement do not allow to meet the short-term emerging needs. Indeed, in a standardpower module, each switching cell requires the manufacture of two chips and two wirebonds for interconnections. Each wirebond is a source of stray inductance and constitutes a limiting factor of electrical performance, a source of an increase in the level of electrical stress and of reliability problems. It is customary to place a decoupling capacitor on the power supply terminals of the switching cell. This capacitor makes it possible to compensate the stray inductance prior to the switching cell but itdoesn’t compensate in any way the stray inductance within the switching cell. This paper proposes for the first time a concept of an ultimate monolithic wire-bondless switching cell that allows to suppress the wirebonds within the switching cell and therefore minimize the stray inductance value. Consequently, it is suited for the emerging applications requiring to achieve simultaneously: reliability, compactness, intrinsic performance and reduced manufacturing cost.

Published
2019

20. Hyperspectral image segmentation based on the estimation of the multiway signal subspace dimension (Conference Presentation)

Author

Salah Bourennane, Caroline Fossati, Pierre Delmas, Institut FRESNEL (FRESNEL), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)

Subjects
Pixel, business.industry, Computer science, Multispectral image, 0211 other engineering and technologies, Hyperspectral imaging, 020206 networking & telecommunications, Pattern recognition, Sobel operator, Image processing, 02 engineering and technology, Image segmentation, Edge detection, Computer Science::Computer Vision and Pattern Recognition, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 0202 electrical engineering, electronic engineering, information engineering, Artificial intelligence, business, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, Signal subspace
Abstract

One of the objectives of image processing is to detect the region of interest (ROI) in the given application, and then perform characterization and classification of these regions. In HyperSpectral Images (HSI) the detection of targets in an image is of great interest for several applications. However, edge detection is not considered by the community. Moreover, it is less easy to define what can be a contour in a HSI than in a conventional image where the edges are defined as a rapid change in the intensity of the pixels. In fact, because the spectral data (hyperspectral pixels) consist of several hundreds of values, it becomes more difficult to define the variations between two pixels. One can found in the literature a few edge detection works on multi or hyperspectral images, based on statistical similarity criteria (maximum likelihood, Mahalanobis distance), or based on a geometric measurement approach (study of distances or angles between pixel vectors), some works also adapt to multispectral data some classical convolution filters (Sobel gradient, etc.), or mathematical morphology methods. But all these methods are more suited to the multispectral than the hyperspectral data because they are put in difficulty by the large amount of information known as Hugues phenomenon. In this paper we propose a new multidimensional method, based on tensor modelling of HSI, and using a local approach thanks to subtensors rank estimation to perform edge detection. This could be a good way to select regions of interest which are known to be useful when small target detection is needed. Generally, when ROI containing targets is previously selected, the detection results are better. The ROI selection for large HSI containing objects with large and small sizes, is based on tensorial modelling, and an estimation of local signal subspace rank variations. Examples of the results obtained by the proposed method show that the local variations of signal subspace rank for subtensors of 5x5 pixels on 143 bands centered on each pixel of the image. The processed real-world HSI containing an almost uniform background and 4 rows of different sizes of panels. The obtained results concerning the evolution of the local rank of subtensors centered on each pixel of the HSI, show that the local rank of the signal subspace and the panels are linked. According to these promising and convincing results, in this paper we propose an algorithm for edge detection based on local rank variations.

Published
2019
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21. Riverine emissions of nitrous oxide in a cropland region in France

Author

Agnès Grossel, Hocine Bourennane, Adeline Ayzac, Gilles Billen, Josette Garnier, Anun Martinez, Catherine Pasquier, Catherine Hénault, Unité de recherche Science du Sol (USS), Institut National de la Recherche Agronomique (INRA), Milieux Environnementaux, Transferts et Interactions dans les hydrosystèmes et les Sols (METIS), Université Pierre et Marie Curie - Paris 6 (UPMC)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Agroécologie [Dijon], and Université de Bourgogne (UB)-Institut National de la Recherche Agronomique (INRA)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement

Subjects
[SDU.STU]Sciences of the Universe [physics]/Earth Sciences, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

22. Alzheimer’s Disease Computer-Aided Diagnosis on Positron Emission Tomography Brain Images Using Image Processing Techniques

Author

Caroline Fossati, Mouloud Adel, Salah Bourennane, Eric Guedj, Imene Garali, Thierry Gaidon, Xiaoxi Pan, Julien Wojak, GSM (GSM), Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Laboratoire d'Astrophysique de Marseille (LAM), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Aix Marseille Université (AMU)-Centre National d'Études Spatiales [Toulouse] (CNES), Service Central de Biophysique et de Médecine Nucléaire, Hôpital de la Timone [CHU - APHM] (TIMONE), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), and Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)

Subjects
medicine.diagnostic_test, business.industry, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, Image processing, 030218 nuclear medicine & medical imaging, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, Positron emission tomography, Computer-aided diagnosis, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Medicine, Computer vision, Artificial intelligence, business, GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries), 030217 neurology & neurosurgery, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019
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23. Cartographie du réservoir utile des sols de France et incertitudes associées

Author

ROMAN DOBARCO, Mercedes, Bourennane, Hocine, Arrouays, Dominique, Cousin, Isabelle, Saby, Nicolas, Martin, Manuel, InfoSol (InfoSol), Institut National de la Recherche Agronomique (INRA), and Unité de recherche Science du Sol (USS)

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, réserve en eau du sol, Earth Sciences, eau du sol, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, réservoir utilisable, réserve utile, Sciences de la Terre, Sciences agricoles, ComputingMilieux_MISCELLANEOUS, Agricultural sciences
Abstract

National audience

Published
2019

24. Remote sensing data integration for soil clay content prediction at a national scale

Author

Loiseau, Thomas, Chen, Songchao, Mulder, Vera Leatitia, Roman Dobarco, Mercedes, Richer-de-Forges, Anne C, Lehmann, Sébastien, Bourennane, Hocine, Saby, Nicolas, Martin, Manuel, Vaudour, Emmanuelle, Gomez, Cécile, Lagacherie, Philippe, Arrouays, Dominique, InfoSol (InfoSol), Institut National de la Recherche Agronomique (INRA), Soil Geography and Landscape Group, Wageningen University and Research [Wageningen] (WUR), Unité de recherche Science du Sol (USS), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
[SDV]Life Sciences [q-bio], [SDE]Environmental Sciences, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

25. Improving Video Object Detection by Seq-Bbox Matching

Author

Heng Zhang, Hatem Belhassen, Virginie Fresse, El-Bay Bourennane, Beamtek, Laboratoire Hubert Curien [Saint Etienne] (LHC), Institut d'Optique Graduate School (IOGS)-Université Jean Monnet [Saint-Étienne] (UJM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, and Fresse, Virginie

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Matching (statistics), business.industry, Computer science, 02 engineering and technology, 010501 environmental sciences, 01 natural sciences, Object detection, [INFO.INFO-ES] Computer Science [cs]/Embedded Systems, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer vision, [INFO.INFO-ES]Computer Science [cs]/Embedded Systems, Artificial intelligence, business, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences
Abstract

International audience

Published
2019

26. Multilinear Side-Information based Discriminant Analysis for face and kinship verification in the wild

Author

Mebarka Belahcene, Elhocine Boutellaa, Mohcene Bessaoudi, Ammar Chouchane, Abdelmalik Ouamane, Salah Bourennane, Institut FRESNEL (FRESNEL), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)

Subjects
Multilinear Side-Information based Discriminant Analysis, 0209 industrial biotechnology, Multilinear map, Current (mathematics), Computer science, Cognitive Neuroscience, 02 engineering and technology, Kinship verification, 020901 industrial engineering & automation, Dimension (vector space), [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Artificial Intelligence, Tensor (intrinsic definition), 0202 electrical engineering, electronic engineering, information engineering, Tensor, Projection (set theory), Face verification, Multilinear subspace projection, ComputingMilieux_MISCELLANEOUS, Tensor analysis, business.industry, Pattern recognition, Linear discriminant analysis, Computer Science Applications, ComputingMethodologies_PATTERNRECOGNITION, Face (geometry), [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 020201 artificial intelligence & image processing, Artificial intelligence, business, Subspace topology
Abstract

This paper presents a new approach for face and kinship verification under unconstrained environments. The proposed approach is based on high order tensor representation of face images. The face tensor is built based on local descriptors extracted at multiscales. Besides, we formulate a novel Multilinear Side-Information based Discriminant Analysis (MSIDA) to handle the weakly supervised multilinear subspace projection and classification. Using only the weak label information, MSIDA projects the input face tensor in a new subspace in which the discrimination is improved and the dimension of each tensor mode is reduced simultaneously. Experimental evaluation on four challenging face databases (LFW, Cornell KinFace, UB KinFace and TSKinface) demonstrates that the proposed approach significantly outperforms the current state of the art.

Published
2019
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27. Dimensionality reduction and coloured noise removal from hyperspectral images

Author

Salah Bourennane, Caroline Fossati, GSM (GSM), Institut FRESNEL (FRESNEL), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Bourennane, Salah, and Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)

Subjects
Multilinear map, Multilinear algebra, business.industry, Noise reduction, Dimensionality reduction, Wiener filter, Hyperspectral imaging, Pattern recognition, Reduction (complexity), Noise, symbols.namesake, Earth and Planetary Sciences (miscellaneous), symbols, Artificial intelligence, Electrical and Electronic Engineering, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, ComputingMilieux_MISCELLANEOUS, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Mathematics
Abstract

Hyperspectral image (HSI) classification requires spectral dimensionality reduction and noise reduction. While common dimensionality reduction (DR) and denoising methods are based on linear algebra, we propose a multilinear algebra method to jointly achieve denoising reduction and DR. Multilinear tools consider multidimensional data as whole entity by processing jointly spatial and spectral ways. However, it cannot cope with the HSIs distorted by non-white noise which is the most realistic case and cannot preserve rare signals. First, we propose a new method for whitening the noise (W) in HSI. Then we propose a method based on multidimensional wavelet packet transform (MWPT) and multiway Wiener filter (MWF) which performs both non-white noise and spectral DR, referred to as W-MWPT-MWFdr-(). The Classification algorithm support vector (SVM) machines are applied to the output of the following DR and noise reduction methods to compare their efficiency: The proposed W-MWPT-MWFdr-(); prewhitening method associ...

Published
2015
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28. Efficient Noise Reduction Method Based on Multilinear Tools for Hyperspectral Imagery

Author

Juan, Josselin, Fossati, Caroline, Bourennane, Salah, GSM (GSM), Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Bourennane, Salah, and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0207 environmental engineering, 02 engineering and technology, 020701 environmental engineering, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, ComputingMilieux_MISCELLANEOUS, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience

Published
2015
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29. A Novel Hybrid Approach for 3D Face Recognition Based on Higher Order Tensor

Author

Mebarka Belahcene, Mohcene Bessaoudi, Ammar Chouchane, Salah Bourennane, Abdelmalik Ouamane, Université Mohamed Khider de Biskra (BISKRA), Institut FRESNEL (FRESNEL), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), University Mohamed Khider Biskra, Algeria, Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), and Bourennane, Salah

Subjects
021110 strategic, defence & security studies, Multilinear map, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, business.industry, Computer science, Dimensionality reduction, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 0211 other engineering and technologies, Pattern recognition, 02 engineering and technology, Linear discriminant analysis, Facial recognition system, Multilinear principal component analysis, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Histogram, Face (geometry), Tensor (intrinsic definition), [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Artificial intelligence, business, ComputingMilieux_MISCELLANEOUS
Abstract

This paper presents a new hybrid approach for 3D face verification based on tensor representation in the presence of illuminations, expressions and occlusion variations. Depth face images are divided into sub-region and the Multi-Scale Local Binarised Statistical Image Features (MSBSIF) histogram are extracted from each sub-region and arranged as a third order tensor. Furthermore, to reduce the dimensionality of this tensor data, we use a novel hybrid approach based on two steps of dimensionality reduction multilinear and non-linear. Firstly, Multilinear Principal Component Analysis (MPCA) is used. MPCA projects the input tensor in a new lower subspace in which the dimension of each tensor mode is reduced. After that, the non-linear Exponential Discriminant Analysis (EDA) is used to discriminate the faces of different persons. Finally, the matching is performed using distance measurement. The proposed approach (MPCA+EDA) has been tested on the challenging face database Bosporus 3D and the experimental results show that our method achieves a high verification performance compared with the state of the art.

Published
2018

30. Investigating channel frequency selectivity in indoor visible‐light communication systems

Author

Zabih Ghassemlooy, Shihe Long, Mohammad-Ali Khalighi, Michael M. Wolf, Salah Bourennane, GSM (GSM), Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), University of Northumbria at Newcastle [United Kingdom], and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Computer science, On-off keying, Optical communication, 02 engineering and technology, Filter (signal processing), Communications system, Atomic and Molecular Physics, and Optics, Delay spread, 020210 optoelectronics & photonics, Interference (communication), Modulation, [SPI.OPTI]Engineering Sciences [physics]/Optics / Photonic, 0202 electrical engineering, electronic engineering, information engineering, Electronic engineering, Electrical and Electronic Engineering, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, ComputingMilieux_MISCELLANEOUS, Communication channel
Abstract

Channel characterisation for indoor visible-light communication systems is revisited. The purpose of this study is to evaluate the channel frequency selectivity, or in other words, the significance of inter-symbol interference (ISI) at the receiver and the necessity of channel equalisation to recover the transmitted data. The authors focus on the effect of the indoor channel by assuming no bandwidth constraint on the light-emitting-diodes and by considering a simple intensity modulation technique, excluding discrete multi-tone modulation. The channel impulse response (CIR) is first simulated using the iterative site-based method. Then, conventional metrics for evaluating channel frequency selectivity, that is, the root-mean-square delay spread and channel frequency response are investigated and their practical interest is discussed. The authors also consider the signal-to-ISI ratio (SIR), which they define based on the sampled (i.e. discrete-time) signal at the receiver, and demonstrate its usefulness in determining the necessity of channel equalisation at the receiver. They consider several link scenarios in a medium-size and a large room, and show the significance of the LOS components of CIR in determining the channel frequency selectivity. They also discuss the choice of the receiver filter and explain how it affects the SIR.

Published
2016
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31. Brain region of interest selection for 18FDG positrons emission tomography computer-aided image classification

Author

Sylvain Takerkart, Mouloud Adel, Eric Guedj, Salah Bourennane, Imene Garali, GSM (GSM), Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Centre Mémoire de Ressources et de Recherche, CHU Marseille, Imagerie MOléculaire pour applications THéranostiques personnalisées (IMOTHEP), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)- Hôpital de la Timone [CHU - APHM] (TIMONE), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)- Hôpital de la Timone [CHU - APHM] (TIMONE)

Subjects
Normalization (statistics), Computer science, Feature extraction, Normalization (image processing), Biomedical Engineering, Biophysics, 02 engineering and technology, computer.software_genre, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Voxel, Histogram, 0202 electrical engineering, electronic engineering, information engineering, medicine, Computer vision, Medical diagnosis, ComputingMilieux_MISCELLANEOUS, Receiver operating characteristic, Contextual image classification, medicine.diagnostic_test, business.industry, Pattern recognition, Random forest, Support vector machine, Positron emission tomography, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Spatial normalization, Kurtosis, 020201 artificial intelligence & image processing, Tomography, Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract

Positron Emission Tomography (PET) imaging is of importance for diagnosing neurodegenerative diseases like Alzheimer Disease (AD). Computer-aided diagnosis methods could process and analyze quantitatively these images, in order to better characterize and extract meaningful information for medical diagnosis. This paper presents a novel computer-aided diagnosis technique for brain PET images classification in the case of AD. Brain images are first segmented into 116 Regions Of Interest (ROI) using an atlas. Computing some statistical features (mean, standard-deviation, skewness, kurtosis and entropy) on these regions' histogram, we define a Separation Power Factor (SPF) associated to each region. This factor quantifies the ability of each region to separate AD from Healthy Control (HC) brain images. Ranking selected regions according to their SPF and inputting them to a Support Vector Machine (SVM) classifier, yields the same or slightly better classification results than when inputting the whole brain voxels or the 116 regions, but with less computational time. Comparison is also done using Random Forest classifier.

Published
2016
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32. Uncertainty assessment of GlobalSoilMap soil available water capacity products: A French case study

Author

M. Román Dobarco, Hocine Bourennane, Nicolas Saby, Isabelle Cousin, Manuel Martin, Dominique Arrouays, InfoSol (InfoSol), Institut National de la Recherche Agronomique (INRA), and Unité de recherche Science du Sol (USS)

Subjects
Soil texture, [SDV]Life Sciences [q-bio], Soil Science, Soil science, 04 agricultural and veterinary sciences, 010501 environmental sciences, 15. Life on land, Silt, 01 natural sciences, Available water capacity, 6. Clean water, Field capacity, Permanent wilting point, Pedotransfer function, 13. Climate action, Digital soil mapping, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Water content, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences
Abstract

Plant available water capacity (AWC) refers to the maximum amount of water that a soil can store and provide to plant roots. Spatial predictions of AWC through digital soil mapping at high resolution and national extent provide relevant information for upscaling ecological and hydrological models, and assessment of the provision of ecosystem services like water quantity and quality regulation, carbon sequestration, and provision of food and raw materials. However, the spatial predictions of AWC are prone to errors and uncertainties. Moreover, this digital soil mapping process requires using pedotransfer functions (PTFs) due to the lack of sufficient georeferenced measurements of the upper (i.e., soil moisture at field capacity, θFC) and lower (i.e., soil moisture at permanent wilting point, θPWP) limits of soil moisture contents defining AWC. This adds an additional source of uncertainty to the final estimates of AWC. The objectives of this study were: 1) to predict AWC for mainland France following the GlobalSoilMap (GSM) project specifications on depth intervals and uncertainty, and 2) to quantify the uncertainty of AWC accounting for uncertainty of the soil input variables and the PTFs' coefficients. We first predicted the soil input properties by GSM layer (0–5, 5–15, 15–30, 30–60, 60–100, 100–200 cm), and then applied PTFs for estimating θFC, θPWP, and volumetric AWC (cm3 cm−3). The volume of coarse elements by GSM layer was subtracted before aggregating AWC to estimated soil depth for a maximum of 2 m. The uncertainty of AWC was quantified by first-order Taylor analysis. Independent evaluation indicated that clay had the lowest R2 (clay R2 = 0.27, silt R2 = 0.43 and sand R2 = 0.46) and RMSE (clay RMSE = 128 g kg−1, silt RMSE = 139 g kg−1 and sand RMSE = 172 g kg−1) from the three particle size fractions. However, the model for coarse elements had the worst predictive performance (R2 = 0.14 and RMSE = 21%) among all AWC input variables. The performance of the GSM predictions for θFC and θPWP had a R2 of 0.21 and 0.29. When the PTFs were applied to the spatial predictions of sand and clay, the RMSE for θFC and θPWP had a relative increase of 25% and 36% respectively compared to when they were applied to measured horizon data. Across the majority of mainland France, the main sources of uncertainty of elementary AWC were coarse elements and soil texture, but the contribution of uncertainty of PTFs' coefficients increased in areas dominated by very sandy and clayey textures. An advantage of the produced maps of θFC, θPWP and AWC is that the end users can incorporate associated uncertainties into ecological and agricultural modelling, and decision-making processes involved in soil and water planning.

Published
2019
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33. Multilinear Enhanced Fisher Discriminant Analysis for robust multimodal 2D and 3D face verification

Author

Mebarka Belahcene, Ammar Chouchane, Abdelmalik Ouamane, Mohcene Bessaoudi, Salah Bourennane, Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Multilinear map, Computer science, business.industry, Dimensionality reduction, Pattern recognition, 02 engineering and technology, Linear discriminant analysis, Matrix (mathematics), Discriminant, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Artificial Intelligence, Tensor (intrinsic definition), Face (geometry), [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 0202 electrical engineering, electronic engineering, information engineering, Multilinear subspace learning, 020201 artificial intelligence & image processing, Tensor, Artificial intelligence, business, ComputingMilieux_MISCELLANEOUS, Curse of dimensionality
Abstract

In the last few years, multilinear subspace learning has attracted large interest for dimensionality reduction and classification of multidimentional data. In this paper, we propose a new multilinear supervised projection method, called Multilinear Enhanced Fisher Discriminant Analysis (MEFDA), which improves the generalization capacity by decomposing the discriminate analysis process into a simultaneous diagonalization of the inter-class and intra-class scatter matrices resulting from the unfolding of tensor data. MEFDA uses the discriminant tensor criterion based on iterative procedure in order to obtain the optimal projection matrix in each k mode of tensor data. One virtue of MEFDA is that it can avoid the disadvantage of large dimensionality, in which the computational cost is reduced. Since the data encoded using high-order tensor, MEFDA respects the original structure and the natural geometry of face data. Therefore, the natural correlation of multi-dimension face data can be exploited. The proposed algorithm has been evaluated on three public 3D face datasets, FRGC V2.0, CASIA 3D and Bosphorus under the hard challenges of poses, expressions, illuminations and occlusions. The experimental results demonstrate that the proposed method significantly outperforms the current state of the art methods, with 99% and 97.02% at 0.001 False Acceptance Rate (FAR) for FRGC v2.0, and Bosphorus datasets, respectively, and 99.36% at evaluation Equal Error Rate (EER) for CASIA 3D dataset.

Published
2018
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34. Switching Cells and Power Devices - An Introduction

Author

Richardeau, Frédéric, Bourennane, Abdelhakim, Université de Toulouse (Université de Toulouse), Centre National de la Recherche Scientifique (CNRS), LAboratoire PLasma et Conversion d'Energie (LAPLACE), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées, Laboratoire d'analyse et d'architecture des systèmes (LAAS), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J), Convertisseurs Statiques (LAPLACE-CS), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse III - Paul Sabatier (UT3), Équipe Intégration de Systèmes de Gestion de l'Énergie (LAAS-ISGE), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), and Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole)

Subjects
[SPI.NRJ]Engineering Sciences [physics]/Electric power, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2018

35. Noise Removal Based on Tensor Modelling for Hyperspectral Image Classification

Author

Salah Bourennane, Caroline Fossati, Tao Lin, GSM (GSM), Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Magma Design Automation, and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
wavelet packet transform, hyperspectral image, signal-dependent noise, multiway Wiener filtering, denoising, classification, Computer science, Noise reduction, 0211 other engineering and technologies, Imaging spectrometer, 02 engineering and technology, Noise (electronics), Wavelet packet decomposition, symbols.namesake, 0202 electrical engineering, electronic engineering, information engineering, lcsh:Science, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, business.industry, Wiener filter, Hyperspectral imaging, Pattern recognition, White noise, Additive white Gaussian noise, Computer Science::Computer Vision and Pattern Recognition, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], symbols, General Earth and Planetary Sciences, 020201 artificial intelligence & image processing, lcsh:Q, Artificial intelligence, business
Abstract

International audience; With the current state-of-the-art computer aided manufacturing tools, the spatial resolution of hyperspectral sensors is becoming increasingly higher thus making it easy to obtain much more detailed information of the scene captured. However, the improvement of the spatial resolution also brings new challenging problems to address with signal dependent photon noise being one of them. Unlike the signal independent thermal noise, the variance of photon noise is dependent on the signal, therefore many denoising methods developed for the stationary noise cannot be applied directly to the photon noise. To make things worse, both photon and thermal noise coexist in the captured hyperspectral image (HSI), thus making it more difficult to whiten noise. In this paper, we propose a new denoising framework to cope with signal dependent nonwhite noise (SDNW), Pre-estimate—Whitening—Post-estimate (PWP) loop, to reduce both photon and thermal noise in HSI. Previously, we proposed a method based on multidimensional wavelet packet transform and multi-way Wiener filter which performs both white noise and spectral dimensionality reduction, referred to as MWPT-MWF, which was restricted to white noise. We get inspired from this MWPT-MWF to develop a new iterative method for reducing photon and thermal noise. Firstly, the hyperspectral noise parameters estimation (HYNPE) algorithm is used to estimate the noise parameters, the SD noise is converted to an additive white Gaussian noise by pre-whitening procedure and then the whitened HSI is denoised by the proposed method SDNW-MWPT-MWF. As comparative experiments, the Multiple Linear Regression (MLR) based denoising method and tensor-based Multiway Wiener Filter (MWF) are also used in the denoising framework. An HSI captured by Reflective Optics System Imaging Spectrometer (ROSIS) is used in the experiments and the denoising performances are assessed from various aspects: the noise whitening performance, the Signal-to-Noise Ratio (SNR), and the classification performance. The results on the real-world airborne hyperspectral image HYDICE (Hyperspectral Digital Imagery Collection Experiment) are also presented and analyzed. These experiments show that it is worth taking into account noise signal-dependency hypothesis for processing HYDICE and ROSIS HSIs.

Published
2018
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36. Improved Discrete Grey Wolf Optimizer

Author

Salah Bourennane, Benoit Martin, Julien Marot, Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), GSM (GSM), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Computer science, 0202 electrical engineering, electronic engineering, information engineering, Process (computing), 020206 networking & telecommunications, 020201 artificial intelligence & image processing, 02 engineering and technology, Algorithm, ComputingMilieux_MISCELLANEOUS, Selection (genetic algorithm)
Abstract

Grey wolf optimizer (GWO) is a bioinspired iterative optimization algorithm which simulates the hunting process of a wolf pack guided by three leaders. In this paper, a novel discrete GWO is proposed: a random leader selection is performed, and the probability for the main leader to be selected increases at the detriment of the other leaders across iterations. The proposed discrete GWO is compared to another discrete version of GWO, using standard test functions.

Published
2018
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37. Combining pedotransfer functions, digital soil mapping, and uncertainty assessment for providing usable maps of available water

Author

ROMAN DOBARCO, Mercedes, Saby, Nicolas, Bourennane, Hocine, Cousin, Isabelle, Arrouays, Dominique, Martin, Manuel, InfoSol (InfoSol), Institut National de la Recherche Agronomique (INRA), and Unité de recherche Science du Sol (USS)

Subjects
[SDV]Life Sciences [q-bio], [SDE]Environmental Sciences, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2018

38. Histogram-Based Features Selection and Volume of Interest Ranking for Brain PET Image Classification

Author

Imene Garali, Mouloud Adel, Salah Bourennane, Eric Guedj, Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), GSM (GSM), Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Service Central de Biophysique et de Médecine Nucléaire, Hôpital de la Timone [CHU - APHM] (TIMONE), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Centre National de la Recherche Scientifique (CNRS), and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
lcsh:Medical technology, positron emission tomography, first order statistics, lcsh:Computer applications to medicine. Medical informatics, Article, feature selection, lcsh:R855-855.5, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, classification, Machine learning, lcsh:R858-859.7, computer-aided diagnosis, Alzheimer’s disease, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, ComputingMilieux_MISCELLANEOUS
Abstract

Positron emission tomography (PET) is a molecular medical imaging modality which is commonly used for neurodegenerative diseases diagnosis. Computer-aided diagnosis, based on medical image analysis, could help quantitative evaluation of brain diseases such as Alzheimer’s disease (AD). A novel method of ranking the effectiveness of brain volume of interest (VOI) to separate healthy control from AD brains PET images is presented in this paper. Brain images are first mapped into anatomical VOIs using an atlas. Histogram-based features are then extracted and used to select and rank VOIs according to the area under curve (AUC) parameter, which produces a hierarchy of the ability of VOIs to separate between groups of subjects. The top-ranked VOIs are then input into a support vector machine classifier. The developed method is evaluated on a local database image and compared to the known selection feature methods. Results show that using AUC outperforms classification results in the case of a two group separation., General scheme of region ranking and feature selection for computer-aided diagnosis of Alzheimer's Disease on PET images.

Published
2018
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39. Efficient time of arrival estimation in the presence of multipath propagation

Author

Caroline Fossati, Salah Bourennane, Guilhem Villemin, GSM (GSM), Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), and Bourennane, Salah

Subjects
Sound Spectrography, Time Factors, Acoustics and Ultrasonics, Rank (linear algebra), Computer science, Acoustics, 0211 other engineering and technologies, 02 engineering and technology, Motion, Matrix (mathematics), Time of arrival, Arts and Humanities (miscellaneous), 0202 electrical engineering, electronic engineering, information engineering, ComputingMilieux_MISCELLANEOUS, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, 021101 geological & geomatics engineering, Water, Signal Processing, Computer-Assisted, 020206 networking & telecommunications, Models, Theoretical, Transfer matrix, Frequency spectrum, Sound, Face (geometry), [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithm, Algorithms, Smoothing, Multipath propagation
Abstract

Most of acoustical experiments face multipath propagation issues. The times of arrival of different ray paths on a sensor can be very close. To estimate them, high resolution algorithms have been developed. The main drawback of these methods is their need of a full rank spectral matrix of the signals. The frequential smoothing technique overcomes this issue by dividing the received signal spectrum into several overlapping sub-bands. This division yields a transfer matrix that may suffer rank deficiency. In this paper, a new criterion to optimally choose the sub-band frequencies is proposed. Encouraging results were obtained on real-world data.

Published
2013
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40. Multidimensional Signal Processing Methods: Target Detection Methods Based on Tensor Decompositions

Author

Fossati, Caroline, Bourennane, Salah, Bourennane, Salah, GSM (GSM), Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2017

41. Iterative Channel Estimation and Data Detection for Amplify-and-Forward Relay Networks

Author

Yi Zhang, Salah Bourennane, Mohammad-Ali Khalighi, GSM (GSM), Institut FRESNEL (FRESNEL), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), and Bourennane, Salah

Subjects
Computer science, Iterative method, Real-time computing, 02 engineering and technology, law.invention, 03 medical and health sciences, 0302 clinical medicine, Relay, law, 0202 electrical engineering, electronic engineering, information engineering, Wireless, Electrical and Electronic Engineering, ComputingMilieux_MISCELLANEOUS, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Computer Science::Information Theory, business.industry, Detector, 020206 networking & telecommunications, Computer Science Applications, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Modeling and Simulation, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithm, Wireless sensor network, 030217 neurology & neurosurgery, Relay channel, Communication channel, Phase-shift keying
Abstract

We consider channel estimation in amplify-and-forward wireless relay networks when non-orthogonal coherent distributed space-time coding is done at relays. We propose to perform iterative channel estimation and data detection based on parallel interference cancelation (PIC) at the receiver. The schemes that we propose are based on soft or hard estimates of the transmitted data symbols. We illustrate the performance improvement compared to the classical approach of pilot-only-based channel estimation and minimum mean-square-error detection. Also, we show that semi-blind channel estimation together with PIC detection is particularly promising for large signal constellations. Moreover, we show that the use of hard estimates of symbols in the proposed iterative receiver provides satisfying results, while requiring a lower computational complexity.

Published
2012
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42. Improvement of Target-Detection Algorithms Based on Adaptive Three-Dimensional Filtering

Author

Salah Bourennane, A Cailly, Caroline Fossati, GSM (GSM), Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Bourennane, Salah, and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
business.industry, Noise (signal processing), Matched filter, 0211 other engineering and technologies, Hyperspectral imaging, 020206 networking & telecommunications, Pattern recognition, 02 engineering and technology, Object detection, Adaptive filter, Signal-to-noise ratio, 0202 electrical engineering, electronic engineering, information engineering, General Earth and Planetary Sciences, Quadtree, Artificial intelligence, Electrical and Electronic Engineering, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithm, ComputingMilieux_MISCELLANEOUS, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, 021101 geological & geomatics engineering, Mathematics, Signal subspace
Abstract

Target detection is a key issue in processing hyperspectral images (HSIs). Spectral-identification-based algorithms are sensitive to spectral variability and noise in acquisition. In most cases, both the target spatial distributions and the spectral signatures are unknown, so each pixel is separately tested and appears as a target when it significantly differs from the background. In this paper, we propose two algorithms to improve the signal-to-noise ratio (SNR) of hyperspectral data, leading to detectors that are robust to noise. These algorithms consist in integrating adaptive spatial/spectral filtering into the adaptive matched filter and adaptive coherence estimator. Considering the HSIs as tensor data, our approach introduces a data representation involving multidimensional processing. It combines the advantages of spatial and spectral information using an alternating least square algorithm. To estimate the signal subspace dimension in each spatial mode, we extend the Akaike information criterion, and we develop an iterative algorithm for spectral-mode rank estimation. We demonstrate the interest of integrating the quadtree decomposition to perform an adaptive 3-D filtering and thereby preserve the local image characteristics. This leads to a significant improvement in terms of denoised tensor SNR and, consequently, in terms of detection probability. The performance of our method is exemplified using simulated and real-world HYperspectral Digital Imagery Collection Experiment images.

Published
2011
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43. Improvement of Classification Based on Noise and Spectral Dimensionality Reduction for Hyperspectral Image

Author

Salah Bourennane, Josselin Juan, Caroline Fossati, GSM (GSM), Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Spatial filter, Computer science, Noise (signal processing), business.industry, Noise reduction, Dimensionality reduction, Wiener filter, Hyperspectral imaging, Pattern recognition, Support vector machine, symbols.namesake, Computer Science::Computer Vision and Pattern Recognition, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], symbols, Artificial intelligence, business, ComputingMilieux_MISCELLANEOUS, Curse of dimensionality
Abstract

Hyperspectral image (HSI) classification requires spectral dimensionalityreduction and spatial filtering. While common dimensionality reduction and denoising methods use linear algebra, we propose a tensorial method to jointly achieve denoising and dimensionality reduction. Firstly, we propose a new method for pre-whitening the noise (PW) in HSI. Then we propose a method based on quadtree decomposition adapted to tensor data in order to take into account the local image characteristics in the multi-way Wiener filter (LMWF) which performs both noise and spectral dimensionality reduction, referred to as PW-LMWFdr-(K1;K2;P3). Classification algorithm SVM is applied to the output of dimensionality and noise reduction methods to compare their efficiency: The proposed PW-LMWFdr-(K1;K2;P3), PW-MWFdr-(K1;K2;P3), PCAdr,MNFdr associated with Wiener filtering.

Published
2018
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44. Implémentation en temps réel d'une architecture embarquée pour le comptage de personnes dans une foule

Author

Gong, Songchenchen, Bourennane, El-Bay, Gong, Songchenchen, Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, and HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement

Subjects
[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2018

45. Improvement of Target Detection Based on Signal Dependent Noise Reduction for Hyperspectral Image

Author

Salah Bourennane, Caroline Fossati, GSM (GSM), Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Noise (signal processing), business.industry, Computer science, Noise reduction, 0211 other engineering and technologies, Hyperspectral imaging, 020206 networking & telecommunications, Pattern recognition, 02 engineering and technology, Physics::Data Analysis, Statistics and Probability, Signal, Statistics::Machine Learning, Transformation (function), Computer Science::Computer Vision and Pattern Recognition, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 0202 electrical engineering, electronic engineering, information engineering, Preprocessor, Artificial intelligence, Minification, business, Linear filter, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering
Abstract

In the hyperspectral images (HSI) acquired by the new-generation hyperspectral sensors the signal dependent noise is an important limitation to the detection. Therefore, noise reduction is an important preprocessing step to analyze the information in the hyperspectral image. A signal dependent noise cannot be reduced by conventional linear filtering. Therefore, a new method based on Parallel factor analysis (PARAFAC) decomposition is proposed to estimate the noise of hyperspectral remote sensing image. Then, the estimated noise is used for whitening the colored structural noise. By using this transformation, the data noise from new-generation hyperspectral sensors is diminished, thereby allowing a minimization of negative impacts on hyperspectral detection applications.

Published
2018
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46. Estimating the Available Water Content by coupling pedological approaches and inverse modelling: the RUEdesSOLS project

Author

Isabelle Cousin, Claude Doussan, Mercedes Roman-Dobarco, Maud Seger, Mohamed Alkacem, Guillaume Coulouma, Alain Bouthier, Christine Le Bas, Philippe Lagacherie, Samuel Buis, Victor Picheny, Julie Constantin, Vincent Rivalland, Luc Champolivier, Hocine Bourennane, Martine Guerif, ProdInra, Migration, Unité de recherche Science du Sol (USS), Institut National de la Recherche Agronomique (INRA), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), InfoSol (InfoSol), Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), ARVALIS - Institut du végétal [Paris], Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDU.STU] Sciences of the Universe [physics]/Earth Sciences, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, [SDE.ES] Environmental Sciences/Environmental and Society, [SDE.ES]Environmental Sciences/Environmental and Society, ComputingMilieux_MISCELLANEOUS
Abstract

National audience

Published
2018

47. About Times of Arrival Estimation for Sources and Buried Objects Localization

Author

Salah Bourennane, Bastien Rouze, Caroline Fossati, GSM (GSM), Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Signal processing, Noise (signal processing), Computer science, Real-time computing, Smart antenna, Array processing, 020206 networking & telecommunications, 02 engineering and technology, Object detection, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Point (geometry), Antenna (radio), Subspace topology, ComputingMilieux_MISCELLANEOUS
Abstract

Sources localization is an important theme of antenna and signal processing researches since decades. It has a lot of applications for source characterization, which leads to object detection, used in submarine field as well as seismic and remote sensing studies. The particularity of array processing is that we are looking at spatio-temporal vector signals which are generated by both radiating sources or reflected by buried objects and ambient medium. These signals are generally corrupted by an additive noise. When the number of sources is greater than the number of the sensors of the smart antenna system, classical methods become unusable. Recently, a new subspace method called incoherent sensor by sensor processing (ISSP) for estimation of directions of arrival (DOAs) is proposed. It is based on a study sensor by sensor to estimate firstly times of arrival(TOAs), then the DOAs of the sources are estimated. The main drawback of this method is the computational load needed to estimate accurately the DOAs. In this paper, we propose new faster methods thanks to the numerical complexity reduction of the existing algorithms. This last point is important nowadays to make embedded systems faster.

Published
2018
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48. Run-time Reconfigurable Network-on-chip: a survey

Author

Leake Kidane, Hiliwi, El-Bey, Bourennane, Kidane, Hiliwi Leake, Laboratoire d'Electronique, d'Informatique et d'Image UMR CNRS 6306 ( Le2i ), Université de Technologie de Belfort-Montbeliard ( UTBM ) -Centre National de la Recherche Scientifique ( CNRS ) -École Nationale Supérieure d'Arts et Métiers ( ENSAM ) -Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Laboratoire Electronique, Informatique et Image ( Le2i ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), Laboratoire d'Electronique, d'Informatique et d'Image [EA 7508] (Le2i), Université de Technologie de Belfort-Montbeliard (UTBM)-Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), and HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement

Subjects
[ INFO ] Computer Science [cs], [INFO]Computer Science [cs], [INFO] Computer Science [cs], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2018

50. Spectral Unmixing of Hyperspectral Images in the Presence of Small Targets

Author

Sylvain Ravel, Caroline Fossati, Salah Bourennane, Institut FRESNEL (FRESNEL), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), GSM (GSM), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), and Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)

Subjects
Endmember, 010504 meteorology & atmospheric sciences, Computer science, detection, 010501 environmental sciences, 01 natural sciences, Spectral line, Non-negative matrix factorization, Matrix decomposition, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Pixel, business.industry, Hyperspectral imaging, Pattern recognition, General Medicine, Unmixing, Thresholding, Hyperspectral, Rare pixel, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Artificial intelligence, business
Abstract

International audience; Generally, the content of the hyperspectral image pixel is a mixture of the reflectance spectra of the different components in the imaged scene. In this paper, we consider a linear mixing model where the pixels are linear combinations of those reflectance spectra, called endmembers, and linear coefficients corresponding to their abundances. An important issue in hyperspectral imagery consists in unmixing those pixels to retrieve the endmembers and their corresponding abundances. We consider the unmixing issue in the presence of small targets, that is, their endmembers are only contained in few pixels of the image. We introduce a thresholding method relying on Non-negative Matrix Factorization to detect pixels containing rare endmembers. We propose two resampling methods based on bootstrap for spectral unmixing of hyperspectral images to retrieve both the dominant and rare endmembers. Our experimental results on both simulated and real world data demonstrate the efficiency of the proposed method to estimate correctly all the endmembers present in hyperspectral images, in particular the rare endmembers.

Published
2018
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