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15 results on '"GIPSA Pôle Sciences des Données (GIPSA-PSD)"'

1. Minimalistic in-flight odometry based on two optic flow sensors along a bouncing trajectory

Author

Bergantin, Lucia, Coquet, Charles, Nègre, Amaury, Raharijaona, Thibaut, Marchand, Nicolas, Ruffier, Franck, Aix Marseille Université (AMU), Institut des Sciences du Mouvement Etienne Jules Marey (ISM), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), GIPSA-Services (GIPSA-Services), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), GIPSA - COntrol, PErception, Robots, navigation and Intelligent Computing (GIPSA-COPERNIC), GIPSA Pôle Sciences des Données (GIPSA-PSD), and Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab)

Subjects
[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [SPI.AUTO]Engineering Sciences [physics]/Automatic
Abstract

International audience; Estimating the distance traveled while navigating in a GPS-deprived environment is key for aerial robotic applications. For drones, this issue is often coupled with weight and computational power constraints, from which stems the importance of minimalistic equipment. In this study, we present a visual odometry strategy based solely on two optic flow magnitudes perceived by two optic flow sensors oriented at ±30° on either side of a drone's vertical axis. As results, (i) we measured the local optic flow divergence and the local translational optic flow respectively as the subtraction and the sum of the two optic flow magnitudes perceived (ii) we validated experimentally the visual odometer on a hexarotor oscillating upand-down while following a 50m-long circular trajectory under three illuminance conditions (117lux, 814lux and 1518lux). The measured optic flow divergence was used to estimate the flight height by means of an Extended Kalman Filter. The estimated flight height scaled the measured translational optic flow, which was integrated to perform minimalistic visual odometry.

Published
2022

2. ImSPOC: A Novel Compact Hyperspectral Camera for the Monitoring of Atmospheric Gases

Author

Gousset, Silvère, Picone, Daniele, Le Coarer, Etienne, Rodrigo, Juana, Voisin, D., Croizé, Laurence, Ferrec, Yann, Dalla Mura, Mauro, Institut de Planétologie et d'Astrophysique de Grenoble (IPAG), Centre National d'Études Spatiales [Toulouse] (CNES)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Météo-France, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), DOTA, ONERA, Université Paris Saclay [Palaiseau], ONERA-Université Paris-Saclay, Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), and Tokyo Institute of Technology [Tokyo] (TITECH)

Subjects
Accident prevention, [PHYS]Physics [physics], Hyperspectral imaging, Monitoring, SPECTROSCOPIE, Hyperspectral imaging sensor, Miniaturized sensor, [SPI]Engineering Sciences [physics], Gas detector, SURVEILLANCE, Fourier transform, Gas monitoring, IMAGERIE HYPERSPECTRALE, DETECTION GAZ, Spectroscopy, PREVENTION ACCIDENT
Abstract

International audience; There is an increasing industrial and scientific demand for large scale monitoring of gas concentration, e.g., to abide with constraints on pollutants for environmental safety. This paper aims to show the potential of the very recent ImSPOC technology for gas monitoring. We present the ImSPOC operating principle, some of the developed prototypes and two illustrative examples in the monitoring of methane and carbon dioxide in controlled environments.

Published
2022

3. A Two-Stream Multiscale Deep Learning Architecture for Pan-Sharpening

Author

Jie Wei, Zhihui Wei, Jocelyn Chanussot, Yang Xu, Zebin Wu, Wanting Cai, Nanjing University of Science and Technology (NJUST), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Apprentissage de modèles à partir de données massives (Thoth), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), and Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )

Subjects
Atmospheric Science, Computer science, Feature extraction, Multispectral image, Convolutional neural network (CNN), Geophysics. Cosmic physics, 0211 other engineering and technologies, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Convolutional neural network, image fusion, 0202 electrical engineering, electronic engineering, information engineering, Computers in Earth Sciences, Subnetwork, Image resolution, TC1501-1800, 021101 geological & geomatics engineering, Spatial resolution, Artificial neural network, business.industry, QC801-809, Deep learning, pan-sharpening, Pattern recognition, Convolution, Ocean engineering, Kernel, Kernel (image processing), multiscale, 020201 artificial intelligence & image processing, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Neural networks
Abstract

International audience; Pan-sharpening, which fuses the high-resolution panchromatic (PAN) image and the low-resolution multispectral image (MSI), is a hot topic in remote sensing. Recently, deep learning technology has been successfully applied in pan-sharpening. However, the existing methods ignore that the MSI and PAN image are at different resolutions and use the same networks to extract features of the two images. To address this problem, we propose a two-stream deep learning architecture, called coupled multiscale convolutional neural network, for pan-sharpening. The proposed network has three components, feature extraction subnetworks, fusion layer, and super-resolution subnetwork. In the feature extraction subnetworks, two subnetworks are used to extract the features of the MSI and PAN image separately. Different sizes of convolutional kernels are used in the first layers due to the different spatial resolutions. Thus, the source images are mapped to the similar scale. Then a multiscale asymmetric convolution factorization is used to extract features at different scales. In the fusion layer, the two feature extraction subnetworks are coupled. Features at the same scale are first summed, and then the features of all scales are concatenated as one feature map. At last, a super-resolution subnetwork is used to generate the high-resolution MSI. Experimental results on both synthetic and real data sets demonstrate that the proposed method outperforms the other state-of-the-art pan-sharpening methods.

Published
2020

4. GRAFT: Unsupervised Adaptation to Resizing for Detection of Image Manipulation

Author

Ludovic Darmet, François Cayre, Kai Wang, GIPSA Pôle Géométrie, Apprentissage, Information et Algorithmes (GIPSA-GAIA), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), GIPSA - Apprentissage, Classification, Traitement d'Images et de Vidéos (GIPSA-ACTIV), GIPSA Pôle Sciences des Données (GIPSA-PSD), and Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab)

Subjects
General Computer Science, Computer science, likelihood maximization, domain adaptation, Pipeline (computing), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Field (computer science), Domain (software engineering), [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], 0202 electrical engineering, electronic engineering, information engineering, General Materials Science, Adaptation (computer science), manipulation detection, business.industry, General Engineering, Process (computing), [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Image forensics, covariance matrices, 020206 networking & telecommunications, Pattern recognition, Mixture model, Gaussian mixture model, 020201 artificial intelligence & image processing, Noise (video), Artificial intelligence, lcsh:Electrical engineering. Electronics. Nuclear engineering, Focus (optics), business, lcsh:TK1-9971
Abstract

International audience; A large number of methods for forensics of image manipulation relies on detecting fingerprints in residuals or noises. Therefore, these detection methods are bound to be sensitive to noise generated by the image acquisition process, as well as any pre-processing. We show that a difference in pre-processing pipelines between training and testing sets induces performance losses for various classifiers. We focus on a particular pre-processing: resizing. It corresponds to a typical scenario where images may be resized (e.g., downscaled to reduce storage) prior to being manipulated. This performance loss due to pre-resizing could be troublesome but has been rarely investigated in the image forensics field. We propose a new and effective adaptation method for one state-of-the-art image manipulation detection pipeline, and we call our proposed method Gaussian mixture model Resizing Adaptation by Fine-Tuning (GRAFT). Adaptation is performed in an unsupervised fashion, i.e., without using any ground-truth label in the pre-resized testing domain, for the detection of image manipulation on very small patches. Experimental results show that the proposed GRAFT method can effectively improve the detection accuracy in this challenging scenario of unsupervised adaptation to resizing pre-processing.

Published
2020

5. Semi-supervised Classification of Hyperspectral Image through Deep Encoder-Decoder and Graph Neural Networks

Author

Imed Riadh Farah, Akrem Sellami, Mauro Dalla Mura, Refka Hanachi, University of Manouba, National School of Computer Science, Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Université Grenoble Alpes (UGA)

Subjects
Pixel, Computer science, business.industry, Feature vector, Deep learning, Feature extraction, Hyperspectral imaging, Pattern recognition, Autoencoder, ComputingMethodologies_PATTERNRECOGNITION, Computer Science::Computer Vision and Pattern Recognition, Graph (abstract data type), Artificial intelligence, Representation (mathematics), business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; In this research study, we deal with remote sensing data analysis over high dimensional space formed by hyperspectral images. This task is generally complex due to the large spectral, spatial richness, and mixed pixels. Thus, several spectral un-mixing methods have been proposed to discriminate mixing spectra by estimating the classes and their presence rates. However, information related to mixed pixel composition is very interesting for some applications, but it is insufficient for many others. Thus, it is necessary to have much more data about the spatial localization of the classes detected during the spectral un-mixing process. To solve the above-mentioned problem and specify the spatial location of the different land cover classes in the mixed pixel, sub-pixel mapping techniques were introduced. This manuscript presents a novel sub-pixel mapping process relying on K-SVD (K-singular value decomposition) learning and total variation as a spatial regularization parameter (SMKSVD-TV: Sub-pixel Mapping based on K-SVD dictionary learning and Total Variation). The proposed approach adopts total variation as a spatial regularization parameter, to make edges smooth, and a pre-constructed spatial dictionary with the K-SVD dictionary training algorithm to have more spatial configurations at the sub-pixel level. It was tested and validated with three real hyperspectral data. The experimental results reveal that the attributes obtained by utilizing a learned spatial dictionary with isotropic total variation allowed improving the classes sub-pixel spatial localization, while taking into account pre-learned spatial patterns. It is also clear that the K-SVD dictionary learning algorithm can be applied to construct a spatial dictionary, particularly for each data set.

Published
2021

6. Smart charging impact on electric vehicles in presence of photovoltaics

Author

Ahmad Hably, Seddik Bacha, Khaled Hajar, Baoling Guo, GIPSA - COntrol, PErception, Robots, navigation and Intelligent Computing (GIPSA-COPERNIC), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Laboratoire de Génie Electrique de Grenoble (G2ELab ), Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Electric vehicles, RESs, 02 engineering and technology, 7. Clean energy, Automotive engineering, [SPI.AUTO]Engineering Sciences [physics]/Automatic, Charging station, Photovoltaics, 0202 electrical engineering, electronic engineering, information engineering, Wind power, business.industry, V2G, [SPI.NRJ]Engineering Sciences [physics]/Electric power, Photovoltaic system, charging station. 1, 020206 networking & telecommunications, Grid, Renewable energy, EV charging program, Electric power transmission, 13. Climate action, Environmental science, 020201 artificial intelligence & image processing, charging station, Electricity, business, plug in electric vehicles
Abstract

International audience; The emergence of electric vehicles (EVs) promises to be the turning point of sustainable energy and, in particular, renewable energy production in the world. EV Charging will generate substantial extra demand for electricity. Renewable energy, including solar and wind power, can be met in the grid technically and economically. Recent studies have shown that smart charging of EV can increase the synergy between photovoltaics (PV), electrical transmission and electricity usage, resulting in technological and economic advantages. Given the increasing emphasis on this area, this analysis summarizes an overview on smart charging studies taking into account PV power output and consumption of electricity.

Published
2021

7. Pursuit-evasion Game for Nonholonomic Mobile Robots With Obstacle Avoidance using NMPC

Author

Ahmad Hably, Mukhtar Sani, Bogdan Robu, GIPSA - COntrol, PErception, Robots, navigation and Intelligent Computing (GIPSA-COPERNIC), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), GIPSA - Modelling and Optimal Decision for Uncertain Systems (GIPSA-MODUS), and GIPSA Pôle Automatique et Diagnostic (GIPSA-PAD)

Subjects
0301 basic medicine, Computer Science::Computer Science and Game Theory, Optimization problem, Computer science, ComputingMilieux_PERSONALCOMPUTING, Pursuer, Mobile robot, [SPI.AUTO]Engineering Sciences [physics]/Automatic, 03 medical and health sciences, symbols.namesake, 030104 developmental biology, 0302 clinical medicine, Control theory, Nash equilibrium, Best response, Obstacle avoidance, Differential game, symbols, Pursuit-evasion, 030217 neurology & neurosurgery
Abstract

International audience; In this work, non-cooperative competitive games between two unmanned ground robots using Nonlinear Model Predictive Control (NMPC) while incorporating obstacle avoidance techniques are studied. The objective of the first player (pursuer) is to minimize the relative distance and orientation between itself and the second player (evader) while avoiding obstacles, whereas the evader does the opposite. The Pursuit-Evasion Game (PEG) being a typical class of a differential game is formulated as a zero-sum game with two homogeneous players in five different game scenarios. The objective function of each player is formulated as a double optimization problem and is solved separately using NMPC techniques. The optimal trajectory of each player is computed iteratively by considering the best response of the opponent player. The level of information is assumed to be symmetric. Simulations of various scenarios show the winning possibility of each player.

Published
2020

8. Toward Cybersecurity of Unmanned Aircraft System operations under 'Specific' category

Author

Jean-Marc Thiriet, Amin El Mrabti, Trung Duc Tran, Nicolas Marchand, GIPSA - Safe, Controlled and Monitored Systems (GIPSA-SAFE), GIPSA Pôle Automatique et Diagnostic (GIPSA-PAD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), GIPSA - COntrol, PErception, Robots, navigation and Intelligent Computing (GIPSA-COPERNIC), GIPSA Pôle Sciences des Données (GIPSA-PSD), SOGILIS company [Grenoble], and Association Nationale Recherche Technologique (ANRT), 33 rue Rennequin -75017 Paris- SOGILIS company (France), 4 Avenue Doyen Louis Weil, 38000 Grenoble

Subjects
Cybersecurity, Computer science, Rulemaking, 020206 networking & telecommunications, 02 engineering and technology, SORA, Computer security, computer.software_genre, Critical infrastructure, Aviation safety, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], Safety risk, Privacy, Specific Operation, Agency (sociology), 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], media_common.cataloged_instance, 020201 artificial intelligence & image processing, UAS, European union, Risk assessment, computer, media_common
Abstract

International audience; Nowadays, the increasing number of Unmanned Aircraft System (UAS) operations raises public concerns on cybersecurity issues. Therefore, it requires a methodology to address these issues during the UAS development. The Specific Operation Risk Assessment (SORA) is a risk assessment methodology developed by Joint Authorities for Rulemaking on Unmanned Aircraft System (JARUS). This methodology is endorsed by the European Union Aviation Safety Agency (EASA) as an acceptable means to fulfill the requirements of EU regulation related to UAS operations under the Specific category. The original SORA methodology focus on Safety risk scenarios only, which relate to unintentional threats and the harms to people’s life. In this paper, we introduce our solution to extend the methodology toward cybersecurity aspects. The extended methodology concerns risk scenarios relating to intentional digital threats and some other harms (e.g privacy violation, damage to critical infrastructure). A part of this solution is developed and is presented in this paper.

Published
2020

9. Gradient-Based Algorithm with Spatial Regularization for Optimal Sensor Placement

Author

Fateme Ghayem, Bertrand Rivet, Christian Jutten, Rodrigo Cabral Farias, GIPSA - Vision and Brain Signal Processing (GIPSA-VIBS), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), IEEE, and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)

Subjects
Alternating optimization, 0209 industrial biotechnology, Signal to noise ratio, Optimization problem, Computer science, 0207 environmental engineering, Penalty method, 02 engineering and technology, Grid, Sensor placement, Regularization (mathematics), Signal extraction, 020901 industrial engineering & automation, Gradient based algorithm, 020701 environmental engineering, Greedy algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithm
Abstract

International audience; In this paper, we are interested in optimal sensor placement for signal extraction. Recently, a new criterion based on output signal to noise ratio has been proposed for sensor placement. However, to solve the optimization problem, a greedy approach is used over a grid, which is not optimal. To improve this method, we present an optimization approach to locate all the sensors at once. We further add a constraint to the problem that controls the average distances between the sensors. To solve our problem, we use an alternating optimization penalty method. As the associated cost function is non-convex, the proposed algorithm should be carefully initialized. We propose to initialize it with the result of the greedy method. Experimental results show the superiority of the proposed method over the greedy approach.

Published
2020

10. Robust hybrid control of parallel inverters for accurate power-sharing in microgrid

Author

Reza Razi, Seddik Bacha, Minh-Cong Pham, Quoc Tuan Tran, Hossein Iman-Eini, Ahmad Hably, Department of Electrical and Computer Engineering, University of Tehran, Laboratoire de Génie Electrique de Grenoble (G2ELab ), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Institut National de L'Energie Solaire (INES), Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), GIPSA - COntrol, PErception, Robots, navigation and Intelligent Computing (GIPSA-COPERNIC), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)

Subjects
conventional droop method, parallel inverters, Computer science, [SPI.NRJ]Engineering Sciences [physics]/Electric power, Automatic frequency control, inductive microgrid, power sharing, 020206 networking & telecommunications, Hardware_PERFORMANCEANDRELIABILITY, 02 engineering and technology, Frequency deviation, [SPI.AUTO]Engineering Sciences [physics]/Automatic, Reliability (semiconductor), Hybrid control, Control theory, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Voltage droop, impedance- power droop, Transient (oscillation), Microgrid, Voltage
Abstract

There are some challenges in control of parallel inverters in high-voltage microgrids such as voltage and frequency deviations, presence of communication links, low reliability, and inaccurate power sharing. This paper proposes a hybrid control method using the impedance-power droop and conventional droop method to cover the problems of both methods as much as possible. In the proposed method, the impedance-power droop acts as the main controller and the conventional droop method is employed as auxiliary controller during startup and transient states. The notable advantages of the proposed method are low virtual impedances, no need for recognition of system parameters, and simple and low calculations. Moreover, the voltage deviation in the steady state is minimized and the frequency deviation is completely eliminated. Finally, the performance of the control method is validated by simulation results. The hybrid method is compared with the both methods individually that shows the superiority of the proposed method.

Published
2020

11. Nonnegative CP decomposition of multiangle hyperspectral data: A case study on CRISM observations of Martian ICY surface

Author

Pierre Comon, Sylvain Douté, Jeremy E. Cohen, R. Cabrai Farias, M. A. Veganzones, Jocelyn Chanussot, Veganzones, Miguel Angel, Tensor Decomposition for Data Analysis with Applications to Health and Environment - DECODA - - EC:FP7:ERC2013-09-01 - 2018-08-31 - 320594 - VALID, SIGMAPHY (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-GIPSA Pôle Sciences des Données (GIPSA-PSD), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), CICS (GIPSA-CICS), Institut de Planétologie et d'Astrophysique de Grenoble (IPAG), Centre National d'Études Spatiales [Toulouse] (CNES)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS), This work is supported by the European Research Council under the European Communitys Seventh Framework Programme FP7/2007-2013 GrantAgreement no. 320594, 'DECODA'. Also, by a contract with PNTS, ANR (AAP Blanc 2012 Inter II - SIMI 5) and a grant from Label OSUG@2020(Investissements davenir ANR10 LABX56), European Project: 320594,EC:FP7:ERC,ERC-2012-ADG_20120216,DECODA(2013), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Communication Information and Complex Systems (GIPSA-CICS), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
Martian, Ground truth, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, CRISM mission, 0211 other engineering and technologies, Hyperspectral imaging, 020206 networking & telecommunications, 02 engineering and technology, Mars Exploration Program, CRISM, law.invention, Orbiter, nonnegative tensor decomposition, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 13. Climate action, law, Martian surface, 0202 electrical engineering, electronic engineering, information engineering, Radiance, Canonical-Polyadic, multi-angle hyperspectral, MARS-ReCO, Geology, 021101 geological & geomatics engineering, Remote sensing
Abstract

International audience; The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) sensor aboard the Mars Reconnaissance Orbiter takes hyperspectral multi-angle acquisitions of Martian surface from the top of the atmosphere (TOA) on visible and infrared wavelengths. The Multiangle Approach for Retrieval of Surface Reflectance from CRISM Observations (MARS-ReCO) defined an innovative TOA radiance model and inversion scheme aimed at correcting for aerosols effects taking advantage of the near-simultaneous multi-angle CRISM observations. Here, we aim to provide validation evidence of MARS-ReCO by unmixing the estimated multi-angle bidi-rectional reflectance (BRF) from highly reflective and anisotropic icy surfaces at high latitudes with grazing illumination, using a non-negative CP decomposition. Obtained results are in accordance with other complementary studies, which compose a collaboration effort to validate MARS-ReCO through the cross-validation of different techniques in the absence of ground truth.

Published
2016

12. Dry snow backscattering sensitivity on density change for SWE estimation

Author

J.-P. Ovarlez, J.-P. Dedieu, Guy d'Urso, Gabriel Vasile, D. Boldo, Srdjan Stankovic, Nikola Besic, Jocelyn Chanussot, SIGMAPHY (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-GIPSA Pôle Sciences des Données (GIPSA-PSD), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Department of Electrical Engineering, University of Montenegro (UCG), Laboratoire d'étude des transferts en hydrologie et environnement (LTHE), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), EDF (EDF), EDF R&D (EDF R&D), Sondra, CentraleSupélec, Université Paris-Saclay (SONDRA), ONERA-CentraleSupélec-Université Paris-Saclay, ONERA - The French Aerospace Lab [Palaiseau], ONERA, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Sondra, CentraleSupélec, Université Paris-Saclay (COmUE) (SONDRA), ONERA-CentraleSupélec-Université Paris Saclay (COmUE), and ONERA-Université Paris Saclay (COmUE)

Subjects
Synthetic aperture radar, 010504 meteorology & atmospheric sciences, Backscatter, 0211 other engineering and technologies, Soil science, 02 engineering and technology, 01 natural sciences, Physics::Geophysics, symbols.namesake, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Dry snow, Rayleigh scattering, SWE, Physics::Atmospheric and Oceanic Physics, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Scattering, snow density, sensitivity, backscattering, Polarization (waves), Snow, [SPI.ELEC]Engineering Sciences [physics]/Electromagnetism, 13. Climate action, Liquid water content, symbols, Environmental science, Astrophysics::Earth and Planetary Astrophysics, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, SAR
Abstract

This paper deals particularly with the sensitivity of the wet snow backscattering coefficient on density change. The presented backscattering model is based on the approach used in the dry snow analysis [1], appropriately modified to account for the increased dielectric contrast caused by liquid water presence. It encircles our undertaking of simulating and analysing snow backscattering using fundamental scattering theories (IEM-B, QCA, QCA-CP). The wet snow parameters are chosen according to the area of the particular interest - the French Alps, while the choice of the SAR sensor parameters (frequency, polarization) is primarily conditioned by the initially settled goal - reaching qualitative conclusions concerning wet snow backscattering mechanism. Based on simulation results, we state the dominance of the snow pack surface backscattering component, causing the backscattering to be directly proportional to the volumetric liquid water content. This result is confirmed by the performed in situ measurements. We illustrate as well the decrease of this effect with the increase in operating frequency.

Published
2012

13. Unsupervised nonlinear spectral unmixing by means of NLPCA applied to hyperspectral imagery

Author

Giorgio Licciardi, Sylvain Douté, Xavier Ceamanos, Jocelyn Chanussot, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Institut de Planétologie et d'Astrophysique de Grenoble (IPAG ), Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), SIGMAPHY (GIPSA-SIGMAPHY), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-GIPSA Pôle Sciences des Données (GIPSA-PSD), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Institut de Planétologie et d'Astrophysique de Grenoble (IPAG), Centre National d'Études Spatiales [Toulouse] (CNES)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), and Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Pixel, business.industry, Computer science, 0211 other engineering and technologies, Process (computing), Hyperspectral imaging, 020206 networking & telecommunications, Pattern recognition, 02 engineering and technology, Nonlinear system, hyperspectral, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Simplicity (photography), Principal component analysis, 0202 electrical engineering, electronic engineering, information engineering, Point (geometry), NLPCA, Artificial intelligence, business, nonlinear unmixing, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 021101 geological & geomatics engineering
Abstract

International audience; In the literature, for sake of simplicity it is usually assumed that the model ruling spectral mixture in a hyperspectral pixels is basically linear. However, in many real life cases the different materials are usually in intimate association, like sand grains, resulting in a nonlinear mixture. Unfortunately, modeling a nonlinear approach is not trivial, and a general procedure is still up to be found. Aim of this paper is to evaluate the potentialities of Nonlinear Principal Component Analysis (NLPCA) as an approach to perform a nonlinear unmixing for the unsupervised extraction and quantification of the endmembers. From this point of view scope of this paper is to demonstrate that the NLPCs derived from the proposed process can be considered as endmembers. To perform an accurate evaluation, the proposed algorithm has been tested on two different hyperspectral datasets and compared with other approaches found in the literature.

Published
2012

14. Mahalanobis kernel based on probabilistic principal component

Author

Mathieu Fauvel, A. Villa, Jon Atli Benediktsson, Jocelyn Chanussot, Dynamiques Forestières dans l'Espace Rural (DYNAFOR), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure Agronomique de Toulouse-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, SIGMAPHY (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-GIPSA Pôle Sciences des Données (GIPSA-PSD), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Faculty of Electrical Engineering, University of Iceland [Reykjavik], Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), École nationale supérieure agronomique de Toulouse [ENSAT], University of Iceland, Institut National Polytechnique de Grenoble (INPG), and Ville service.

Subjects
hyperspectral image, Software_OPERATINGSYSTEMS, Computer science, Kernel density estimation, 0211 other engineering and technologies, 02 engineering and technology, 01 natural sciences, Kernel principal component analysis, 010104 statistics & probability, Kernel (linear algebra), symbols.namesake, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Polynomial kernel, mahalanobis kernel, Gaussian function, support vector machine, Mean-shift, 0101 mathematics, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, Hyperparameter, Mahalanobis distance, business.industry, Covariance matrix, probabilistic principal component analysis, Pattern recognition, [SDE.ES]Environmental Sciences/Environmental and Society, Support vector machine, Kernel method, Kernel (image processing), Variable kernel density estimation, Kernel embedding of distributions, Kernel (statistics), Principal component analysis, Radial basis function kernel, Kernel smoother, symbols, Kernel regression, Principal component regression, Artificial intelligence, spectral dimension, Kernel Fisher discriminant analysis, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

A kernel adapted to the spectral dimension of hyperspectral images is proposed in this paper. A distance based on a statistical cluster model is used to construct a radial kernel. This class specific kernel realizes a compromise between a conventional Gaussian kernel and a Gaussian kernel on the first principal components of the considered class. An automatic gradient optimization is used to select the optimal hyperparameters. Experimental results on a real hyperspectral image show the kernel is effective compared to the conventional Gaussian kernel. Furthermoren the proposed kernel is less sensitive to one hyperparameter compared to the Gaussian kernel applied on the first principal components of the data.

Published
2011

15. Multivariate dictionary learning and shift & 2D rotation invariant sparse coding

Author

David Mercier, Aurélien Mayoue, Quentin Barthélemy, Jerome Mars, Anthony Larue, SIGMAPHY (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-GIPSA Pôle Sciences des Données (GIPSA-PSD), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Laboratoire Outils d'Analyse des Données (LOAD), Département Métrologie Instrumentation & Information (DM2I), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Université Paris-Saclay-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Laboratoire Information, Modèles, Apprentissage [Gif-sur-Yvette] (LIMA), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST)

Subjects
Shift-invariant, Multivariate statistics, Sparse coding, 02 engineering and technology, 03 medical and health sciences, statistical analysis, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], 0202 electrical engineering, electronic engineering, information engineering, Invariant (mathematics), signal processing, Mathematics, 030307 microscopy, 0303 health sciences, Approximation theory, Signal processing, K-SVD, Rotation-invariant, business.industry, Handwritten characters, Approximation algorithm, 020206 networking & telecommunications, Pattern recognition, artificial intelligence, Levenberg-Marquardt, Levenberg–Marquardt algorithm, machine learning, Multivariate DLA, artifical intelligence, Online learning, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Multivariate OMP, Artificial intelligence, dictionary learning, business, Neural coding, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; In this article, we present a new tool for sparse coding : Multivariate DLA which empirically learns the characteristic patterns associated to a multivariate signals set. Once learned, Multivariate OMP approximates sparsely any signal of this considered set. These methods are specified to the 2D rotation-invariant case. Shift and rotation invariant cases induce a compact learned dictionary. Our methods are applied to 2D handwritten data in order to extract the elementary features of this signals set.

Published
2011

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