37 results on '"Plaza, Javier"'
Search Results
2. Distributed Nonlocal Coupled Hierarchical Tucker Decomposition for Hyperspectral Image Fusion.
- Author
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Zheng, Peng, Sun, Jin, Xu, Yang, Zhang, Yi, Wei, Zhihui, Plaza, Javier, Plaza, Antonio, and Wu, Zebin
- Abstract
Hyperspectral image (HIS) super-resolution aims to fuse a low-spatial-resolution HSI (LR-HSI) and a high-spatial-resolution multispectral image (HR-MSI) to obtain a high-resolution HSI (HR-HSI). Tensor-based methods have demonstrated their outstanding ability to construct the relationship between the LR-HSI and the HR-MSI. This letter introduces a nonlocal hierarchical Tucker decomposition (HTD) model for hyperspectral and multispectral image (HSI-MSI) fusion. First, similar nonlocal patch tensors are clustered according to their similarity in the HR-MSI. Next, the spatial/spectral relationship between the LR-HSI and the HR-MSI is extracted through HTD. The alternating direction method of multipliers (ADMMs) is employed to solve the proposed model. Furthermore, to overcome the high computational complexity of the model solver, we propose an efficient distributed and parallel method to accelerate the fusion process. Experimental results demonstrate that the proposed method not only substantially outperforms state-of-the-art HSI-MSI fusion methods, but also achieves a significant acceleration rate. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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3. Cloud implementation of the K-means algorithm for hyperspectral image analysis
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Haut, Juan Mario, Paoletti, Mercedes, Plaza, Javier, and Plaza, Antonio
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- 2017
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4. Recent Developments in Endmember Extraction and Spectral Unmixing
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Plaza, Antonio, Martín, Gabriel, Plaza, Javier, Zortea, Maciel, Sánchez, Sergio, Prasad, Saurabh, editor, Bruce, Lori M., editor, and Chanussot, Jocelyn, editor
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- 2011
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5. Hyperspectral Anomaly Detection With Relaxed Collaborative Representation.
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Wu, Zhaoyue, Su, Hongjun, Tao, Xuanwen, Han, Lirong, Paoletti, Mercedes E., Haut, Juan M., Plaza, Javier, and Plaza, Antonio
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ANOMALY detection (Computer security) ,INTRUSION detection systems (Computer security) ,REMOTE sensing ,IMAGE reconstruction ,DETECTORS - Abstract
Anomaly detection has become an important remote sensing application due to the abundant spectral and spatial information contained in hyperspectral images. Recently, hyperspectral anomaly detection methods based on the collaborative representation (CR) model have attracted significant attention. Nevertheless, these methods have to face two main challenges: 1) all features (spectral signatures) are constrained to share the same representation coefficient, which ignores the differences among features and 2) existing dictionaries for pixel-by-pixel detection models are usually not reliable. To address these issues, this article proposes a new relaxed CR detector for hyperspectral anomaly detection by using a novel nonglobal dictionary. The proposed detector conducts CR on each feature dimension of the pixel under test and simultaneously constrains the coding vectors of different features to be similar. To the best of our knowledge, this is the first time that a detection model is built from each feature dimension. To adjust the contributions of each feature, an adaptive feature weight constrained version of the method is also proposed. The nonglobal dictionary is constructed by combining the ${k}$ -nearest neighbor method and an existing global dictionary, which is more reliable and practical than the widely used dual-window dictionary. In addition, this article also designs a band selection strategy for the proposed method. Experiments on five real datasets indicate that the proposed method suppresses background well and outperforms other classical and state-of-the-art methods. [ABSTRACT FROM AUTHOR]
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- 2022
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6. On Endmember Identification in Hyperspectral Images Without Pure Pixels: A Comparison of Algorithms
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Plaza, Javier, Hendrix, Eligius M. T., García, Inmaculada, Martín, Gabriel, and Plaza, Antonio
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- 2012
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7. Improving the Performance of Hyperspectral Image and Signal Processing Algorithms Using Parallel, Distributed and Specialized Hardware-Based Systems
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Plaza, Antonio, Plaza, Javier, and Vegas, Hugo
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- 2010
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8. Parallel morphological/neural processing of hyperspectral images using heterogeneous and homogeneous platforms
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Plaza, Javier, Pérez, Rosa, Plaza, Antonio, Martínez, Pablo, and Valencia, David
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- 2008
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9. Impact of platform heterogeneity on the design of parallel algorithms for morphological processing of high-dimensional image data
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Plaza, Antonio, Plaza, Javier, and Valencia, David
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- 2007
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10. Ghostnet for Hyperspectral Image Classification.
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Paoletti, Mercedes E., Haut, Juan M., Pereira, Nuno S., Plaza, Javier, and Plaza, Antonio
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CONVOLUTIONAL neural networks ,CLASSIFICATION ,KEY performance indicators (Management) ,REMOTE sensing - Abstract
Hyperspectral imaging (HSI) is a competitive remote sensing technique in several fields, from Earth observation to health, robotic vision, and quality control. Each HSI scene contains hundreds of (narrow) contiguous spectral bands. The amount of data generated by HSI devices is often both a solution and a problem for a given application. Extracting information from HSI data cubes is a complex and computationally demanding problem. To tackle this challenge, convolutional neural networks (CNNs) have been widely applied to HSI classification. Despite their success, CNNs are computationally demanding algorithms with high memory requirements due to their large number of internal parameters. The recent interest in using HSI devices in mobile and embedded systems for air and spaceborne platforms turned the attention to computationally lightweight CNN architectures with good classification accuracy. In this article, we present a contribution in that direction. The proposed method combines the ghost-module architecture with a CNN-based HSI classifier to reduce the computational cost and, simultaneously, achieves an efficient classification method with high performance. Our new method is evaluated against nine standard HSI classifiers, and five improved deep-CNN architectures, over five commonly used HSI data sets for algorithm benchmarking. Conducted experiments show that the proposed method exhibits similar or better performance than the other classifiers, achieving top values in the considered performance metrics—even for very limited training sets—and, most importantly, with a fraction of the computational cost. Our novel approach for HSI classification is a strong candidate for implementation on systems with limited computational resources. [ABSTRACT FROM AUTHOR]
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- 2021
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11. On the Evaluation of Machine Learning Algorithms for Hyperspectral Image Classification on a Heterogeneous Computing Device.
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Pereira, Nuno, Haut, Juan M., Plaza, Javier, and Plaza, Antonio
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MACHINE learning ,COMPUTER algorithms ,HYPERSPECTRAL imaging systems ,REMOTE sensing ,DATA analysis - Abstract
Hyperspectral imaging (HSI) systems collect electromagnetic radiation, emitted or reflected by the scene under observation at, typically, hundreds of contiguous and regularly spaced narrow spectral bands, from the visible to the infra-red region of the spectrum. The result is a 3D data cube with two spatial dimensions and a wavelength dimension, composed of hundreds of co-registered images containing information on the spectral signature of the materials present in the field of view of the sensor at the time of capture. Retrieving this information in an efficient way, requires compute systems capable of dealing with a huge amount of data and complex algorithms. The recent development of single board computers (SBC) with enough compute power to deal with HSI processing using machine learning (ML) algorithms opens new opportunities for embedded HSI systems for Earth Observation remote sensing, laboratory setups and industrial applications. In this work, we evaluate the performance of the ODROID-XU4, a heterogeneous computing device, running ML algorithms for HSI classification. The experimental results show the potential of SBC as alternative hardware platforms for applications requiring low power and low cost devices, such as onboard processing units and edge computing. [ABSTRACT FROM AUTHOR]
- Published
- 2021
12. FLOP-Reduction Through Memory Allocations Within CNN for Hyperspectral Image Classification.
- Author
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Paoletti, Mercedes E., Haut, Juan M., Tao, Xuanwen, Plaza, Javier, and Plaza, Antonio
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CONVOLUTIONAL neural networks ,CLASSIFICATION ,COMPUTATIONAL complexity - Abstract
Convolutional neural networks (CNNs) have proven to be a powerful tool for the classification of hyperspectral images (HSIs). The CNN kernels are able to naturally include spatial information to smooth out the spectral variability and the noise present in HSI data. However, these kernels are composed of a large number of learning parameters that must be correctly adjusted to achieve good performance. This forces the model to consume a large amount of training data, being prone to overfitting when limited labeled samples are available. In addition, the execution of kernels is computationally very expensive, increasing quadratically with respect to the size of the convolution filter. This significantly reduces the performance of the model. To overcome the aforementioned limitations, this work presents a new few-parameter CNN (based on shift operations) for HSI classification that dramatically reduces both the number of parameters and the computational complexity of the model in terms of floating-point operations (FLOPs). The operational module combines a shift kernel (which adjusts the input data in particular directions without involving any parameters nor FLOPs) with pointwise convolutions that perform the feature extraction stage. The newly developed shift-based CNN has been employed to conduct HSI classification over five widely used and challenging data sets, achieving very promising results in terms of computational performance and classification accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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13. Neighboring Region Dropout for Hyperspectral Image Classification.
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Paoletti, Mercedes E., Haut, Juan M., Plaza, Javier, and Plaza, Antonio
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Deep neural networks (DNNs) exhibit great performance in the task of hyperspectral image (HSI) classification. However, these models are usually overparameterized and require large amounts of training data in order to properly avoid the curse of dimensionality and the variability of spectral signatures, thus suffering from overfitting problems when very few training samples are available, due to poor generalization ability in this particular case. The traditional regularization dropout (DO) strategy has been shown to be effective in fully connected DNNs but not in convolutional-based ones. This is mainly due to the way these architectures manage the spatial information. In this letter, we introduce a new approach to improve the generalization of convolutional-based models for HSI classification. Specifically, we develop a neighboring region DO technique that selectively cuts off certain neighboring outputs, creating spatial dropped regions. Our experimental results with two well-known HSIs reveal that the newly proposed method helps to achieve better classification accuracy than the traditional DO strategy, with a low computational cost. [ABSTRACT FROM AUTHOR]
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- 2020
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14. A Single Model CNN for Hyperspectral Image Denoising.
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Maffei, Alessandro, Haut, Juan M., Paoletti, Mercedes Eugenia, Plaza, Javier, Bruzzone, Lorenzo, and Plaza, Antonio
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IMAGE denoising ,ARTIFICIAL neural networks ,SOURCE code ,IMAGE analysis - Abstract
Denoising is a common preprocessing step prior to the analysis and interpretation of hyperspectral images (HSIs). However, the vast majority of methods typically adopted for HSI denoising exploit architectures originally developed for grayscale or RGB images, exhibiting limitations when processing high-dimensional HSI data cubes. In particular, traditional methods do not take into account the high spectral correlation between adjacent bands in HSIs, which leads to unsatisfactory denoising performance as the rich spectral information present in HSIs is not fully exploited. To overcome this limitation, this article considers deep learning models—such as convolutional neural networks (CNNs)—to perform spectral–spatial HSI denoising. The proposed model, called HSI single denoising CNN (HSI-SDeCNN), efficiently takes into consideration both the spatial and spectral information contained in HSIs. Experimental results on both synthetic and real data demonstrate that the proposed HSI-SDeCNN outperforms other state-of-the-art HSI denoising methods. Source code: https://github.com/mhaut/HSI-SDeCNN [ABSTRACT FROM AUTHOR]
- Published
- 2020
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15. Neural Ordinary Differential Equations for Hyperspectral Image Classification.
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Paoletti, Mercedes E., Haut, Juan Mario, Plaza, Javier, and Plaza, Antonio
- Abstract
Advances in deep learning (DL) have allowed for the development of more complex and powerful neural architectures. The adoption of deep convolutional-based architectures with residual learning [residual networks (ResNets)] has reached the state-of-the-art performance in hyperspectral image (HSI) classification. Traditionally, ResNets have been considered as stacks of discrete layers, where each one obtains a hidden state of the input data. This formulation must deal with very deep networks, which suffer from an important data degradation as they become deeper. Moreover, these complex models exhibit significant requirements in terms of memory due to the amount of parameters that need to be fine tuned. This leads to inadequate generalization and loss of accuracy. In order to address these issues, this article redesigns the ResNet as a continuous-time evolving model, where hidden representations (or states) are obtained with respect to time (understood as the depth of the network) through the evaluation of an ordinary differential equation (ODE), which is combined with a deep neural architecture. Our experimental results, conducted with four well-known HSI data sets, indicate that redefining deep networks as continuous systems through ODEs offers flexibility when processing and classifying these kinds of remotely sensed data, achieving significant performance even when a very few training samples are available. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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16. Hyperspectral Image Classification Using Random Occlusion Data Augmentation.
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Haut, Juan Mario, Paoletti, Mercedes E., Plaza, Javier, Plaza, Antonio, and Plaza, Li
- Abstract
Convolutional neural networks (CNNs) have become a powerful tool for remotely sensed hyperspectral image (HSI) classification due to their great generalization ability and high accuracy. However, owing to the huge amount of parameters that need to be learned and to the complex nature of HSI data itself, these approaches must deal with the important problem of overfitting, which can lead to inadequate generalization and loss of accuracy. In order to mitigate this problem, in this letter, we adopt random occlusion, a recently developed data augmentation (DA) method for training CNNs, in which the pixels of different rectangular spatial regions in the HSI are randomly occluded, generating training images with various levels of occlusion and reducing the risk of overfitting. Our results with two well-known HSIs reveal that the proposed method helps to achieve better classification accuracy with low computational cost. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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17. Capsule Networks for Hyperspectral Image Classification.
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Paoletti, Mercedes E., Haut, Juan Mario, Fernandez-Beltran, Ruben, Plaza, Javier, Plaza, Antonio, Li, Jun, and Pla, Filiberto
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HYPERSPECTRAL imaging systems ,ARTIFICIAL neural networks ,REMOTE sensing ,MACHINE learning ,NUMERICAL analysis - Abstract
Convolutional neural networks (CNNs) have recently exhibited an excellent performance in hyperspectral image classification tasks. However, the straightforward CNN-based network architecture still finds obstacles when effectively exploiting the relationships between hyperspectral imaging (HSI) features in the spectral–spatial domain, which is a key factor to deal with the high level of complexity present in remotely sensed HSI data. Despite the fact that deeper architectures try to mitigate these limitations, they also find challenges with the convergence of the network parameters, which eventually limit the classification performance under highly demanding scenarios. In this paper, we propose a new CNN architecture based on spectral–spatial capsule networks in order to achieve a highly accurate classification of HSIs while significantly reducing the network design complexity. Specifically, based on Hinton’s capsule networks, we develop a CNN model extension that redefines the concept of capsule units to become spectral–spatial units specialized in classifying remotely sensed HSI data. The proposed model is composed by several building blocks, called spectral–spatial capsules, which are able to learn HSI spectral–spatial features considering their corresponding spatial positions in the scene, their associated spectral signatures, and also their possible transformations. Our experiments, conducted using five well-known HSI data sets and several state-of-the-art classification methods, reveal that our HSI classification approach based on spectral–spatial capsules is able to provide competitive advantages in terms of both classification accuracy and computational time. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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18. Deep Pyramidal Residual Networks for Spectral–Spatial Hyperspectral Image Classification.
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Paoletti, Mercedes E., Haut, Juan Mario, Fernandez-Beltran, Ruben, Plaza, Javier, Plaza, Antonio J., and Pla, Filiberto
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FEATURE extraction ,ARTIFICIAL neural networks ,HYPERSPECTRAL imaging systems ,IMAGE processing ,REMOTE sensing - Abstract
Convolutional neural networks (CNNs) exhibit good performance in image processing tasks, pointing themselves as the current state-of-the-art of deep learning methods. However, the intrinsic complexity of remotely sensed hyperspectral images still limits the performance of many CNN models. The high dimensionality of the HSI data, together with the underlying redundancy and noise, often makes the standard CNN approaches unable to generalize discriminative spectral–spatial features. Moreover, deeper CNN architectures also find challenges when additional layers are added, which hampers the network convergence and produces low classification accuracies. In order to mitigate these issues, this paper presents a new deep CNN architecture specially designed for the HSI data. Our new model pursues to improve the spectral–spatial features uncovered by the convolutional filters of the network. Specifically, the proposed residual-based approach gradually increases the feature map dimension at all convolutional layers, grouped in pyramidal bottleneck residual blocks, in order to involve more locations as the network depth increases while balancing the workload among all units, preserving the time complexity per layer. It can be seen as a pyramid, where the deeper the blocks, the more feature maps can be extracted. Therefore, the diversity of high-level spectral–spatial attributes can be gradually increased across layers to enhance the performance of the proposed network with the HSI data. Our experiments, conducted using four well-known HSI data sets and 10 different classification techniques, reveal that our newly developed HSI pyramidal residual model is able to provide competitive advantages (in terms of both classification accuracy and computational time) over the state-of-the-art HSI classification methods [ABSTRACT FROM AUTHOR]
- Published
- 2019
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19. Estimating Nonlinearities in p-Linear Hyperspectral Mixtures.
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Marinoni, Andrea, Plaza, Javier, Plaza, Antonio, and Gamba, Paolo
- Subjects
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AIR quality , *URBANIZATION , *WATER pollution , *HYPERSPECTRAL imaging systems , *REFLECTANCE - Abstract
Accurately estimating the elements in Earth observations is crucial when assessing specific features such as air quality index, water pollution, or urbanization process behavior. Moreover, physical–chemical composition can be retrieved from hyperspectral images when proper spectral unmixing architectures are employed. Specifically, when linear and nonlinear combinations of endmembers (pure spectral components) are accurately characterized, hyperspectral unmixing plays a key role in understanding and quantifying phenomena occurring over the instantaneous field-of-view. Thus, reliable detection of nonlinear reflectance behavior can play a key role in enhancing hyperspectral unmixing performance. In this paper, two new methods for adaptive design of mixture models for hyperspectral unmixing are introduced. One of the methods relies on exploiting geometrical features of hyperspectral signatures in terms of nonorthogonal projections onto the space induced by the endmembers’ spectra. Then, an iterative process aims at understanding the order of local nonlinearity that is displayed by each endmember over every pixel. An improved version of an artificial neural network-based approach for nonlinearity order information is also considered and compared. Experimental results show that the proposed approaches are actually able to retrieve thorough information on the nature of the nonlinear effects over the image, while providing excellent performance in reconstructing the given data sets. [ABSTRACT FROM AUTHOR]
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- 2018
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20. Active Learning With Convolutional Neural Networks for Hyperspectral Image Classification Using a New Bayesian Approach.
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Haut, Juan Mario, Paoletti, Mercedes E., Plaza, Javier, Li, Jun, and Plaza, Antonio
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HYPERSPECTRAL imaging systems ,REMOTE sensing ,SPECTROMETERS ,SURFACE of the earth ,ACTIVE learning - Abstract
Hyperspectral imaging is a widely used technique in remote sensing in which an imaging spectrometer collects hundreds of images (at different wavelength channels) for the same area on the surface of the earth. In the last two decades, several methods (unsupervised, supervised, and semisupervised) have been proposed to deal with the hyperspectral image classification problem. Supervised techniques have been generally more popular, despite the fact that it is difficult to collect labeled samples in real scenarios. In particular, deep neural networks, such as convolutional neural networks (CNNs), have recently shown a great potential to yield high performance in the hyperspectral image classification. However, these techniques require sufficient labeled samples in order to perform properly and generalize well. Obtaining labeled data is expensive and time consuming, and the high dimensionality of hyperspectral data makes it difficult to design classifiers based on limited samples (for instance, CNNs overfit quickly with small training sets). Active learning (AL) can deal with this problem by training the model with a small set of labeled samples that is reinforced by the acquisition of new unlabeled samples. In this paper, we develop a new AL-guided classification model that exploits both the spectral information and the spatial-contextual information in the hyperspectral data. The proposed model makes use of recently developed Bayesian CNNs. Our newly developed technique provides robust classification results when compared with other state-of-the-art techniques for hyperspectral image classification. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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21. Hyperspectral Unmixing Based on Dual-Depth Sparse Probabilistic Latent Semantic Analysis.
- Author
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Fernandez-Beltran, Ruben, Plaza, Antonio, Plaza, Javier, and Pla, Filiberto
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REMOTE sensing ,HYPERSPECTRAL imaging systems ,LATENT semantic analysis ,INFORMATION retrieval ,IMAGING systems - Abstract
This paper presents a novel approach for spectral unmixing of remotely sensed hyperspectral data. It exploits probabilistic latent topics in order to take advantage of the semantics pervading the latent topic space when identifying spectral signatures and estimating fractional abundances from hyperspectral images. Despite the contrasted potential of topic models to uncover image semantics, they have been merely used in hyperspectral unmixing as a straightforward data decomposition process. This limits their actual capabilities to provide semantic representations of the spectral data. The proposed model, called dual-depth sparse probabilistic latent semantic analysis (DEpLSA), makes use of two different levels of topics to exploit the semantic patterns extracted from the initial spectral space in order to relieve the ill-posed nature of the unmixing problem. In other words, DEpLSA defines a first level of deep topics to capture the semantic representations of the spectra, and a second level of restricted topics to estimate endmembers and abundances over this semantic space. An experimental comparison in conducted using the two standard topic models and the seven state-of-the-art unmixing methods available in the literature. Our experiments, conducted using four different hyperspectral images, reveal that the proposed approach is able to provide competitive advantages over available unmixing approaches. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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22. Stacked Nonnegative Sparse Autoencoders for Robust Hyperspectral Unmixing.
- Author
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Su, Yuanchao, Marinoni, Andrea, Li, Jun, Plaza, Javier, and Gamba, Paolo
- Abstract
As an unsupervised learning tool, autoencoder has been widely applied in many fields. In this letter, we propose a new robust unmixing algorithm that is based on stacked nonnegative sparse autoencoders (NNSAEs) for hyperspectral data with outliers and low signal-to-noise ratio. The proposed stacked autoencoders network contains two main steps. In the first step, a series of NNSAE is used to detect the outliers in the data. In the second step, a final autoencoder is performed for unmixing to achieve the endmember signatures and abundance fractions. By taking advantage from nonnegative sparse autoencoding, the proposed approach can well tackle problems with outliers and low noise-signal ratio. The effectiveness of the proposed method is evaluated on both synthetic and real hyperspectral data. In comparison with other unmixing methods, the proposed approach demonstrates competitive performance. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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23. A New Spatial–Spectral Feature Extraction Method for Hyperspectral Images Using Local Covariance Matrix Representation.
- Author
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Fang, Leyuan, He, Nanjun, Li, Shutao, Plaza, Antonio J., and Plaza, Javier
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HYPERSPECTRAL imaging systems ,FEATURE extraction ,COVARIANCE matrices ,SPECTRAL imaging ,PRINCIPAL components analysis - Abstract
In this paper, a novel local covariance matrix (CM) representation method is proposed to fully characterize the correlation among different spectral bands and the spatial–contextual information in the scene when conducting feature extraction (FE) from hyperspectral images (HSIs). Specifically, our method first projects the HSI into a subspace, using the maximum noise fraction method. Then, for each test pixel in the subspace, its most similar neighboring pixels (within a local spatial window) are clustered using the cosine distance measurement. The test pixel and its neighbors are used to calculate a local CM for FE purposes. Each nondiagonal entry in the matrix characterizes the correlation between different spectral bands. Finally, these matrices are used as spatial–spectral features and fed to a support vector machine for classification purposes. The proposed method offers a new strategy to characterize the spatial–spectral information in the HSI prior to classification. Experimental results have been conducted using three publicly available hyperspectral data sets for classification, indicating that the proposed method can outperform several state-of-the-art techniques, especially when the training samples available are limited. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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24. Multicore Real-Time Implementation of a Full Hyperspectral Unmixing Chain.
- Author
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Bernabe, Sergio, Jimenez, Luis Ignacio, Garcia, Carlos, Plaza, Javier, and Plaza, Antonio
- Abstract
Solving the mixture problem in remotely sensed hyperspectral images remains a challenging task. In particular, solutions are needed in order to obtain a response for applications with real-time constraints. In the last decade, several efforts have been developed, many of them using graphics processing units (GPUs) and focused on the exploitation of spectral information alone. However, a few spectral unmixing chains have been developed using other architectures such as multicore processors, field programmable gate arrays, or Intel Xeon Phi coprocessors. In this letter, we develop a new parallel unmixing chain for multicore processors. Compared with other approaches, the proposed spatial–spectral alternative takes advantage of the complementary information provided by the spatial correlation of the pixels in the image in addition to the spectral information. Our implementation has been optimized using the application program interface OpenMP and the Intel Math Kernel Library on two multicore architectures, and using real analysis scenarios. The results reveal competitive real-time performance compared with another compute unified device architecture implementation previously developed for GPUs. [ABSTRACT FROM PUBLISHER]
- Published
- 2018
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25. GPU Parallel Implementation of Spatially Adaptive Hyperspectral Image Classification.
- Author
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Wu, Zebin, Shi, Linlin, Li, Jun, Wang, Qicong, Sun, Le, Wei, Zhihui, Plaza, Javier, and Plaza, Antonio
- Abstract
Image classification is a very important tool for remotely sensed hyperspectral image processing. Techniques able to exploit the rich spectral information contained in the data, as well as its spatial-contextual information, have shown success in recent years. Due to the high dimensionality of hyperspectral data, spectral-spatial classification techniques are quite demanding from a computational viewpoint. In this paper, we present a computationally efficient parallel implementation for a spectral-spatial classification method based on spatially adaptive Markov random fields (MRFs). The method learns the spectral information from a sparse multinomial logistic regression classifier, and the spatial information is characterized by modeling the potential function associated with a weighted MRF as a spatially adaptive vector total variation function. The parallel implementation has been carried out using commodity graphics processing units (GPUs) and the NVIDIA's Compute Unified Device Architecture. It optimizes the work allocation and input/output transfers between the central processing unit and the GPU, taking full advantages of the computational power of GPUs as well as the high bandwidth and low latency of shared memory. As a result, the algorithm exploits the massively parallel nature of GPUs to achieve significant acceleration factors (higher than 70x) with regards to the serial and multicore versions of the same classifier on an NVIDIA Tesla K20C platform. [ABSTRACT FROM PUBLISHER]
- Published
- 2018
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26. Fusion of Hyperspectral and LiDAR Data Using Sparse and Low-Rank Component Analysis.
- Author
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Rasti, Behnood, Ghamisi, Pedram, Plaza, Javier, and Plaza, Antonio
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HYPERSPECTRAL imaging systems ,LIDAR ,MULTISENSOR data fusion ,SENSOR networks ,OPTICAL radar ,SUPPORT vector machines - Abstract
The availability of diverse data captured over the same region makes it possible to develop multisensor data fusion techniques to further improve the discrimination ability of classifiers. In this paper, a new sparse and low-rank technique is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR)-derived features. The proposed fusion technique consists of two main steps. First, extinction profiles are used to extract spatial and elevation information from hyperspectral and LiDAR data, respectively. Then, the sparse and low-rank technique is utilized to estimate the low-rank fused features from the extracted ones that are eventually used to produce a final classification map. The proposed approach is evaluated over an urban data set captured over Houston, USA, and a rural one captured over Trento, Italy. Experimental results confirm that the proposed fusion technique outperforms the other techniques used in the experiments based on the classification accuracies obtained by random forest and support vector machine classifiers. Moreover, the proposed approach can effectively classify joint LiDAR and hyperspectral data in an ill-posed situation when only a limited number of training samples are available. [ABSTRACT FROM PUBLISHER]
- Published
- 2017
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27. Robust Matrix Discriminative Analysis for Feature Extraction From Hyperspectral Images.
- Author
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Hang, Renlong, Liu, Qingshan, Sun, Yubao, Yuan, Xiaotong, Pei, Hucheng, Plaza, Javier, and Plaza, Antonio
- Abstract
Linear discriminative analysis (LDA) is an effective feature extraction method for hyperspectral image (HSI) classification. Most of the existing LDA-related methods are based on spectral features, ignoring spatial information. Recently, a matrix discriminative analysis (MDA) model has been proposed to incorporate the spatial information into the LDA. However, due to sensor interferers, calibration errors, and other issues, HSIs can be noisy. These corrupted data easily degrade the performance of the MDA. In this paper, a robust MDA (RMDA) model is proposed to address this important issue. Specifically, based on the prior knowledge that the pixels in a small spatial neighborhood of the HSI lie in a low-rank subspace, a denoising model is first employed to recover the intrinsic components from the noisy HSI. Then, the MDA model is used to extract discriminative spatial–spectral features from the recovered components. Besides, different HSIs exhibit different spatial contextual structures, and even a single HSI may contain both large and small homogeneous regions simultaneously. To sufficiently describe these multiscale spatial structures, a multiscale RMDA model is further proposed. Experiments have been conducted using three widely used HSIs, and the obtained results show that the proposed method allows for a significant improvement in the classification performance when compared to other LDA-based methods. [ABSTRACT FROM PUBLISHER]
- Published
- 2017
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28. Parallel Implementation of Spatial?Spectral Endmember Extraction on Graphic Processing Units.
- Author
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Jimenez, Luis Ignacio, Sanchez, Sergio, Martan, Gabriel, Plaza, Javier, and Plaza, Antonio J.
- Abstract
The identification of pure spectral signatures (endmembers) in remotely sensed hyperspectral images has traditionally focused on the spectral information alone. Recently, techniques such as the spatial–spectral endmember extraction (SSEE) have incorporated both the spectral and the spatial information contained in the scene. Since hyperspectral images contain very detailed information in the spatial and spectral domain, the integration of these two sources of information generally comes with a significant increase in computational complexity. In this paper, we develop a new computationally efficient implementation of SSEE using commodity graphics processing units (GPUs). The relevance of GPUs comes from their very low cost, compact size, and the possibility to obtain significant acceleration factors by exploiting properly the GPU hardware architecture. Our experimental results, focused on evaluating the candidate endmembers produced by SSEE and also the computational performance of the GPU implementation, indicated significant acceleration factors that allow exploiting the SSEE method in computationally efficient fashion. [ABSTRACT FROM PUBLISHER]
- Published
- 2017
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29. GPU Implementation of Spatial–Spectral Preprocessing for Hyperspectral Unmixing.
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Jimenez, Luis Ignacio, Martin, Gabriel, Sanchez, Sergio, Garcia, Carlos, Bernabe, Sergio, Plaza, Javier, and Plaza, Antonio
- Abstract
Spectral unmixing pursues the identification of spectrally pure constituents, called
endmembers , and their correspondingabundances in each pixel of a hyperspectral image. Most unmixing techniques have focused on the exploitation of spectral information alone. Recently, some techniques have been developed to take advantage of the complementary information provided by the spatial correlation of the pixels in the image. Computational complexity represents a major problem in these spatial–spectral techniques, as hyperspectral images contain very rich information in both the spatial and spectral domains. In this letter, we develop a computationally efficient implementation of a spatial–spectral processing algorithm that has been successfully applied prior to the spectral unmixing of the hyperspectral data. Our implementation has been optimized for the commodity graphics processing units (GPUs) and is evaluated (using both synthetic and real data) using different GPU architectures. Significant speedups can be achieved when processing hyperspectral images of different sizes. This allows for the inclusion of the proposed parallel preprocessing module in a full hyperspectral unmixing chain able to operate in real time. [ABSTRACT FROM PUBLISHER]- Published
- 2016
- Full Text
- View/download PDF
30. Fast Spatial Preprocessing for Spectral Unmixing of Hyperspectral Data on Graphics Processing Units.
- Author
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Delgado, Jaime, Martin, Gabriel, Plaza, Javier, Jimenez, Luis Ignacio, and Plaza, Antonio
- Abstract
Spectral unmixing is an important technique for hyperspectral data exploitation. It amounts at finding a set of pure spectral signatures (endmembers) of the most representative materials in the scene, and estimating their abundance fractions. The integration of spatial information prior to spectral unmixing of hyperspectral data has attracted much attention in recent years. Several approaches have been developed for the purpose of guiding endmember identification algorithms to spatially representative, yet spectrally pure endmembers. In particular, the spatial preprocessing (SPP) algorithm can be used prior to most existing spectral-based endmember identification techniques, thus promoting the selection of endmembers in spatially representative areas of the scene. However, most SPP techniques are computationally expensive, which adds significant burden to the spectral unmixing process. In this paper, we present three parallel implementations of SPP that have been specifically developed for commodity graphics processing units (GPUs). We perform an evaluation of these techniques using two GPU architectures from NVidia: GeForce GTX 580 and GeForce GTX 870M, which reveals that real-time processing performance can be obtained for real hyperspectral data sets collected by the airborne visible infra-red imaging spectrometer (AVIRIS). [ABSTRACT FROM PUBLISHER]
- Published
- 2016
- Full Text
- View/download PDF
31. Nonlinear Hyperspectral Unmixing Using Nonlinearity Order Estimation and Polytope Decomposition.
- Author
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Marinoni, Andrea, Plaza, Javier, Plaza, Antonio, and Gamba, Paolo
- Abstract
Nonlinear hyperspectral unmixing (HSU) plays a key-role in understanding and quantifying the physical-chemical phenomena occurring over geometrically complex fields of view. Nonlinear HSU methods that do not rely on prior knowledge of the ground truth to analyze the scene are especially interesting. However, they can be affected either by overfitting or performance degradation provided by inaccurate setting of unmixing parameters. In this paper, we introduce a new nonlinear HSU architecture which aims at taking advantage of the benefit provided by the combination of polytope decomposition (POD) method together with artificial neural network (ANN)-based learning. Specifically, ANN is able to efficiently estimate the order $p$ of the nonlinearity provided by the given scene even without the thorough knowledge of the ground truth. The ANN-based learning is used to feed the POD in order to deliver accurate unmixing based on a $p$-linear polynomial model. Experimental results over simulated and real scenes show promising performance of the proposed framework. [ABSTRACT FROM PUBLISHER]
- Published
- 2015
- Full Text
- View/download PDF
32. Improving the scalability of hyperspectral imaging applications on heterogeneous platforms using adaptive run-time data compression
- Author
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Plaza, Antonio, Plaza, Javier, and Paz, Abel
- Subjects
- *
PARALLEL computers , *ADAPTIVE computing systems , *DATA compression , *REMOTE sensing , *WAVELETS (Mathematics) , *MATHEMATICAL transformations , *IMAGE processing - Abstract
Abstract: Latest generation remote sensing instruments (called hyperspectral imagers) are now able to generate hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. In previous work, we have reported that the scalability of parallel processing algorithms dealing with these high-dimensional data volumes is affected by the amount of data to be exchanged through the communication network of the system. However, large messages are common in hyperspectral imaging applications since processing algorithms are pixel-based, and each pixel vector to be exchanged through the communication network is made up of hundreds of spectral values. Thus, decreasing the amount of data to be exchanged could improve the scalability and parallel performance. In this paper, we propose a new framework based on intelligent utilization of wavelet-based data compression techniques for improving the scalability of a standard hyperspectral image processing chain on heterogeneous networks of workstations. This type of parallel platform is quickly becoming a standard in hyperspectral image processing due to the distributed nature of collected hyperspectral data as well as its flexibility and low cost. Our experimental results indicate that adaptive lossy compression can lead to improvements in the scalability of the hyperspectral processing chain without sacrificing analysis accuracy, even at sub-pixel precision levels. [Copyright &y& Elsevier]
- Published
- 2010
- Full Text
- View/download PDF
33. Parallel heterogeneous CBIR system for efficient hyperspectral image retrieval using spectral mixture analysis.
- Author
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Plaza, Antonio J., Plaza, Javier, and Paz, Abel
- Subjects
CONTENT-based image retrieval ,DATABASES ,REMOTE sensing ,COMPUTER networks ,COMPUTER architecture ,IMAGE databases ,BEOWULF clusters (Computer systems) - Abstract
The purpose of content-based image retrieval (CBIR) is to retrieve, from real data stored in a database, information that is relevant to a query. In remote sensing applications, the wealth of spectral information provided by latest-generation (hyperspectral) instruments has quickly introduced the need for parallel CBIR systems able to effectively retrieve features of interest from ever-growing data archives. To address this need, this paper develops a new parallel CBIR system that has been specifically designed to be run on heterogeneous networks of computers (HNOCs). These platforms have soon become a standard computing architecture in remote sensing missions due to the distributed nature of data repositories. The proposed heterogeneous system first extracts an image feature vector able to characterize image content with sub-pixel precision using spectral mixture analysis concepts, and then uses the obtained feature as a search reference. The system is validated using a complex hyperspectral image database, and implemented on several networks of workstations and a Beowulf cluster at NASA's Goddard Space Flight Center. Our experimental results indicate that the proposed parallel system can efficiently retrieve hyperspectral images from complex image databases by efficiently adapting to the underlying parallel platform on which it is run, regardless of the heterogeneity in the compute nodes and communication links that form such parallel platform. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
- View/download PDF
34. Multi-Channel Morphological Profiles for Classification of Hyperspectral Images Using Support Vector Machines.
- Author
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Plaza, Javier, Plaza, Antonio J., and Barra, Cristina
- Subjects
- *
WAVELENGTHS , *SUPPORT vector machines , *REMOTE sensing , *ASTRONOMY , *TECHNOLOGICAL innovations , *HIGH technology , *COMMUNICATION - Abstract
Hyperspectral imaging is a new remote sensing technique that generates hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. Supervised classification of hyperspectral image data sets is a challenging problem due to the limited availability of training samples (which are very difficult and costly to obtain in practice) and the extremely high dimensionality of the data. In this paper, we explore the use of multi-channel morphological profiles for feature extraction prior to classification of remotely sensed hyperspectral data sets using support vector machines (SVMs). In order to introduce multi-channel morphological transformations, which rely on ordering of pixel vectors in multidimensional space, several vector ordering strategies are investigated. A reduced implementation which builds the multi-channel morphological profile based on the first components resulting from a dimensional reduction transformation applied to the input data is also proposed. Our experimental results, conducted using three representative hyperspectral data sets collected by NASA's Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) sensor and the German Digital Airborne Imaging Spectrometer (DAIS 7915), reveal that multi-channel morphological profiles can improve single-channel morphological profiles in the task of extracting relevant features for classification of hyperspectral data using small training sets. [ABSTRACT FROM AUTHOR]
- Published
- 2009
- Full Text
- View/download PDF
35. Hyperspectral Image Classification Using Parallel Autoencoding Diabolo Networks on Multi-Core and Many-Core Architectures.
- Author
-
Torti, Emanuele, Fontanella, Alessandro, Plaza, Antonio, Plaza, Javier, and Leporati, Francesco
- Subjects
HYPERSPECTRAL imaging systems ,IMAGE processing ,MACHINE learning ,REMOTE sensing ,ENCODING - Abstract
One of the most important tasks in hyperspectral imaging is the classification of the pixels in the scene in order to produce thematic maps. This problem can be typically solved through machine learning techniques. In particular, deep learning algorithms have emerged in recent years as a suitable methodology to classify hyperspectral data. Moreover, the high dimensionality of hyperspectral data, together with the increasing availability of unlabeled samples, makes deep learning an appealing approach to process and interpret those data. However, the limited number of labeled samples often complicates the exploitation of supervised techniques. Indeed, in order to guarantee a suitable precision, a large number of labeled samples is normally required. This hurdle can be overcome by resorting to unsupervised classification algorithms. In particular, autoencoders can be used to analyze a hyperspectral image using only unlabeled data. However, the high data dimensionality leads to prohibitive training times. In this regard, it is important to realize that the operations involved in autoencoders training are intrinsically parallel. Therefore, in this paper we present an approach that exploits multi-core and many-core devices in order to achieve efficient autoencoders training in hyperspectral imaging applications. Specifically, in this paper, we present new OpenMP and CUDA frameworks for autoencoder training. The obtained results show that the CUDA framework provides a speed-up of about two orders of magnitudes as compared to an optimized serial processing chain. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
36. Parallel Hyperspectral Image and Signal Processing [Applications Corner].
- Author
-
Plaza, Antonio, Plaza, Javier, Paz, Abel, and Sanchez, Sergio
- Abstract
Remotely sensed hyperspectral imaging instruments are capable of collecting hundreds of images corresponding to different wavelength channels for the same area on the surface of the Earth. For instance, NASA is continuously gathering high-dimensional image data with instruments such as the Jet Propulsion Laboratory's Airborne Visible-Infrared Imaging Spectrometer (AVIRIS). This advanced sensor for Earth observation records the visible and near-infrared spectrum of the reflected light using more than 200 spectral bands, thus producing a stack of images in which each pixel (vector) is represented by a spectral signal that uniquely characterizes the underlying objects. [ABSTRACT FROM PUBLISHER]
- Published
- 2011
- Full Text
- View/download PDF
37. Commodity cluster-based parallel processing of hyperspectral imagery
- Author
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Plaza, Antonio, Valencia, David, Plaza, Javier, and Martinez, Pablo
- Subjects
- *
PARALLEL algorithms , *IMAGING systems , *SPECTRUM analysis , *PARALLEL processing - Abstract
Abstract: The rapid development of space and computer technologies has made possible to store a large amount of remotely sensed image data, collected from heterogeneous sources. In particular, NASA is continuously gathering imagery data with hyperspectral Earth observing sensors such as the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) or the Hyperion imager aboard Earth Observing-1 (EO-1) spacecraft. The development of fast techniques for transforming the massive amount of collected data into scientific understanding is critical for space-based Earth science and planetary exploration. This paper describes commodity cluster-based parallel data analysis strategies for hyperspectral imagery, a new class of image data that comprises hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. An unsupervised technique that integrates the spatial and spectral information in the image data using multi-channel morphological transformations is parallelized and compared to other available parallel algorithms. The code''s portability, reusability and scalability are illustrated by using two high-performance parallel computing architectures: a distributed memory, multiple instruction multiple data (MIMD)-style multicomputer at European Center for Parallelism of Barcelona, and a Beowulf cluster at NASA''s Goddard Space Flight Center. Experimental results suggest that Beowulf clusters are a source of computational power that is both accessible and applicable to obtaining results in valid response times in information extraction applications from hyperspectral imagery. [Copyright &y& Elsevier]
- Published
- 2006
- Full Text
- View/download PDF
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