The Experts below are selected from a list of 10494 Experts worldwide ranked by ideXlab platform
Lorenzo Bruzzone - One of the best experts on this subject based on the ideXlab platform.
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Extended Attribute profiles on gpu applied to hyperspectral image classification
The Journal of Supercomputing, 2019Co-Authors: Pedro G Bascoy, Pablo Quesadabarriuso, Dora B Heras, Francisco Arguello, Begum Demir, Lorenzo BruzzoneAbstract:Extended profiles are an important technique for modelling the spatial information of hyperspectral images at different levels of detail. They are used extensively as a preprocessing stage, especially in classification schemes. In particular, Attribute profiles, based on the application of morphological Attribute filters to the connected components of the image, have been shown to provide very good results. In this paper we present a parallel implementation of the Attribute profiles in CUDA for multispectral and hyperspectral imagery considering the Attributes area and standard deviation. The profile computation is based on the max-tree approach but without building the tree itself. Instead, a matrix-based data structure is used along with a recursive flooding (component merging) and filter process. Additionally, a previous feature extraction stage based on wavelets is applied to the hyperspectral image in order to extract the most valuable spectral information, reducing the size of the resulting profile. This scheme efficiently exploits the thousands of available threads on the GPU, obtaining a considerable reduction in execution time as compared to the OpenMP CPU implementation.
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automatic generation of standard deviation Attribute profiles for spectral spatial classification of remote sensing data
IEEE Geoscience and Remote Sensing Letters, 2013Co-Authors: Prashanth Reddy Marpu, Jon Atli Benediktsson, Mauro Dalla Mura, Mattia Pedergnana, Lorenzo BruzzoneAbstract:Extended Attribute profiles, which are based on Attribute filters, have recently been presented as efficient tools for spectral-spatial classification of remote sensing images. However, construction of these profiles usually requires manual selection of parameters for the corresponding Attribute filters. In this letter, we present a technique to automatically build the Extended Attribute profiles with the standard deviation Attribute based on the statistics of the samples belonging to the classes of interest. The methodology is tested on two widely used hyperspectral images and the results are found to be highly accurate.
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classification of hyperspectral data using Extended Attribute profiles based on supervised and unsupervised feature extraction techniques
International Journal of Image and Data Fusion, 2012Co-Authors: Prashanth Reddy Marpu, Jon Atli Benediktsson, Mauro Dalla Mura, Mattia Pedergnana, Stijn Peeters, Lorenzo BruzzoneAbstract:The classification of remote sensing data based on the exploitation of spatial features extracted with morphological and Attribute profiles has been recently gaining importance. With the development of efficient algorithms to construct the profiles for large datasets, such methods are becoming even more relevant. When dealing with hyperspectral imagery, the profiles are traditionally built on the first few principal components computed from the data. However, it needs to be determined if other feature reduction approaches are better suited to create base images for the profiles. In this article, we explore the use of profiles based on features derived from three supervised feature extraction techniques (i.e. Discriminant Analysis Feature Extraction, Decision Boundary Feature Extraction and Non-parametric Weighted Feature Extraction) and two unsupervised feature-extraction techniques (i.e. Principal Component Analysis (PCA) and Kernel PCA) in classification and compare the classification accuracies obtaine...
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classification of remote sensing optical and lidar data using Extended Attribute profiles
IEEE Journal of Selected Topics in Signal Processing, 2012Co-Authors: Mattia Pedergnana, Jon Atli Benediktsson, Prashanth Reddy Marpu, Dalla M Mura, Lorenzo BruzzoneAbstract:Extended Attribute Profiles (EAPs), which are obtained by applying morphological Attribute filters to an image in a multilevel architecture, can be used for the characterization of the spatial characteristics of objects in a scene. EAPs have proved to be discriminant features when considered for thematic classification in remote sensing applications especially when dealing with very high resolution images. Altimeter data (such as LiDAR) can provide important information, which being complementary to the spectral one can be valuable for a better characterization of the surveyed scene. In this paper, we propose a technique performing a classification of the features extracted with EAPs computed on both optical and LiDAR images, leading to a fusion of the spectral, spatial and elevation data. The experiments were carried out on LiDAR data along either with a hyperspectral and a multispectral image acquired on a rural and urban area of the city of Trento (Italy), respectively. The classification accuracies obtained pointed out the effectiveness of the features extracted by EAPs on both optical and LiDAR data for classification.
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hierarchical analysis of remote sensing data morphological Attribute profiles and binary partition trees
International Symposium on Memory Management, 2011Co-Authors: Jon Atli Benediktsson, Mauro Dalla Mura, Lorenzo Bruzzone, Jocelyn Chanussot, Philippe Salembier, Silvia ValeroAbstract:The new generation of very high resolution sensors in airborne or satellite remote sensing open the door to countless new applications with a high societal impact. In order to bridge the gap between the potential offered by these new sensors and the needs of the end-users to actually face tomorrow's challenges, advanced image processing methods need to be designed. In this paper we discuss two of the most promising strategies aiming at a hierarchical description and analysis of remote sensing data, namely the Extended Attribute Profiles (EAP) and the Binary Partition Trees (BPT). The EAP computes for each pixel a vector of Attributes providing a local multiscale representation of the information and hence leading to a fine description of the local structures of the image. Using different Attributes allows to address different contexts or applications. The BPTs provide a complete hierarchical description of the image, from the pixels (the leaves) to larger regions as the merging process goes on. The pruning of the tree provides a partition of the image and can address various goals (segmentation, object extraction, classification). The EAP and BPT approaches are used in experiments and the obtained results demonstrate their importance.
Jon Atli Benediktsson - One of the best experts on this subject based on the ideXlab platform.
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hyperspectral data classification using Extended extinction profiles
IEEE Geoscience and Remote Sensing Letters, 2016Co-Authors: Pedram Ghamisi, Roberto Souza, Jon Atli Benediktsson, Leticia Rittner, Roberto De Alencar LotufoAbstract:This letter proposes a new approach for the spectral–spatial classification of hyperspectral images, which is based on a novel extrema-oriented connected filtering technique, entitled as Extended extinction profiles . The proposed approach progressively simplifies the first informative features extracted from hyperspectral data considering different Attributes. Then, the classification approach is applied on two well-known hyperspectral data sets, i.e., Pavia University and Indian Pines, and compared with one of the most powerful filtering approaches in the literature, i.e., Extended Attribute profiles. Results indicate that the proposed approach is able to efficiently extract spatial information for the classification of hyperspectral images automatically and swiftly. In addition, an array-based node-oriented max-tree representation was carried out to efficiently implement the proposed approach.
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automatic generation of standard deviation Attribute profiles for spectral spatial classification of remote sensing data
IEEE Geoscience and Remote Sensing Letters, 2013Co-Authors: Prashanth Reddy Marpu, Jon Atli Benediktsson, Mauro Dalla Mura, Mattia Pedergnana, Lorenzo BruzzoneAbstract:Extended Attribute profiles, which are based on Attribute filters, have recently been presented as efficient tools for spectral-spatial classification of remote sensing images. However, construction of these profiles usually requires manual selection of parameters for the corresponding Attribute filters. In this letter, we present a technique to automatically build the Extended Attribute profiles with the standard deviation Attribute based on the statistics of the samples belonging to the classes of interest. The methodology is tested on two widely used hyperspectral images and the results are found to be highly accurate.
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classification of hyperspectral data using Extended Attribute profiles based on supervised and unsupervised feature extraction techniques
International Journal of Image and Data Fusion, 2012Co-Authors: Prashanth Reddy Marpu, Jon Atli Benediktsson, Mauro Dalla Mura, Mattia Pedergnana, Stijn Peeters, Lorenzo BruzzoneAbstract:The classification of remote sensing data based on the exploitation of spatial features extracted with morphological and Attribute profiles has been recently gaining importance. With the development of efficient algorithms to construct the profiles for large datasets, such methods are becoming even more relevant. When dealing with hyperspectral imagery, the profiles are traditionally built on the first few principal components computed from the data. However, it needs to be determined if other feature reduction approaches are better suited to create base images for the profiles. In this article, we explore the use of profiles based on features derived from three supervised feature extraction techniques (i.e. Discriminant Analysis Feature Extraction, Decision Boundary Feature Extraction and Non-parametric Weighted Feature Extraction) and two unsupervised feature-extraction techniques (i.e. Principal Component Analysis (PCA) and Kernel PCA) in classification and compare the classification accuracies obtaine...
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classification of remote sensing optical and lidar data using Extended Attribute profiles
IEEE Journal of Selected Topics in Signal Processing, 2012Co-Authors: Mattia Pedergnana, Jon Atli Benediktsson, Prashanth Reddy Marpu, Dalla M Mura, Lorenzo BruzzoneAbstract:Extended Attribute Profiles (EAPs), which are obtained by applying morphological Attribute filters to an image in a multilevel architecture, can be used for the characterization of the spatial characteristics of objects in a scene. EAPs have proved to be discriminant features when considered for thematic classification in remote sensing applications especially when dealing with very high resolution images. Altimeter data (such as LiDAR) can provide important information, which being complementary to the spectral one can be valuable for a better characterization of the surveyed scene. In this paper, we propose a technique performing a classification of the features extracted with EAPs computed on both optical and LiDAR images, leading to a fusion of the spectral, spatial and elevation data. The experiments were carried out on LiDAR data along either with a hyperspectral and a multispectral image acquired on a rural and urban area of the city of Trento (Italy), respectively. The classification accuracies obtained pointed out the effectiveness of the features extracted by EAPs on both optical and LiDAR data for classification.
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hierarchical analysis of remote sensing data morphological Attribute profiles and binary partition trees
International Symposium on Memory Management, 2011Co-Authors: Jon Atli Benediktsson, Mauro Dalla Mura, Lorenzo Bruzzone, Jocelyn Chanussot, Philippe Salembier, Silvia ValeroAbstract:The new generation of very high resolution sensors in airborne or satellite remote sensing open the door to countless new applications with a high societal impact. In order to bridge the gap between the potential offered by these new sensors and the needs of the end-users to actually face tomorrow's challenges, advanced image processing methods need to be designed. In this paper we discuss two of the most promising strategies aiming at a hierarchical description and analysis of remote sensing data, namely the Extended Attribute Profiles (EAP) and the Binary Partition Trees (BPT). The EAP computes for each pixel a vector of Attributes providing a local multiscale representation of the information and hence leading to a fine description of the local structures of the image. Using different Attributes allows to address different contexts or applications. The BPTs provide a complete hierarchical description of the image, from the pixels (the leaves) to larger regions as the merging process goes on. The pruning of the tree provides a partition of the image and can address various goals (segmentation, object extraction, classification). The EAP and BPT approaches are used in experiments and the obtained results demonstrate their importance.
Mauro Dalla Mura - One of the best experts on this subject based on the ideXlab platform.
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automatic generation of standard deviation Attribute profiles for spectral spatial classification of remote sensing data
IEEE Geoscience and Remote Sensing Letters, 2013Co-Authors: Prashanth Reddy Marpu, Jon Atli Benediktsson, Mauro Dalla Mura, Mattia Pedergnana, Lorenzo BruzzoneAbstract:Extended Attribute profiles, which are based on Attribute filters, have recently been presented as efficient tools for spectral-spatial classification of remote sensing images. However, construction of these profiles usually requires manual selection of parameters for the corresponding Attribute filters. In this letter, we present a technique to automatically build the Extended Attribute profiles with the standard deviation Attribute based on the statistics of the samples belonging to the classes of interest. The methodology is tested on two widely used hyperspectral images and the results are found to be highly accurate.
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classification of hyperspectral data using Extended Attribute profiles based on supervised and unsupervised feature extraction techniques
International Journal of Image and Data Fusion, 2012Co-Authors: Prashanth Reddy Marpu, Jon Atli Benediktsson, Mauro Dalla Mura, Mattia Pedergnana, Stijn Peeters, Lorenzo BruzzoneAbstract:The classification of remote sensing data based on the exploitation of spatial features extracted with morphological and Attribute profiles has been recently gaining importance. With the development of efficient algorithms to construct the profiles for large datasets, such methods are becoming even more relevant. When dealing with hyperspectral imagery, the profiles are traditionally built on the first few principal components computed from the data. However, it needs to be determined if other feature reduction approaches are better suited to create base images for the profiles. In this article, we explore the use of profiles based on features derived from three supervised feature extraction techniques (i.e. Discriminant Analysis Feature Extraction, Decision Boundary Feature Extraction and Non-parametric Weighted Feature Extraction) and two unsupervised feature-extraction techniques (i.e. Principal Component Analysis (PCA) and Kernel PCA) in classification and compare the classification accuracies obtaine...
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hierarchical analysis of remote sensing data morphological Attribute profiles and binary partition trees
International Symposium on Memory Management, 2011Co-Authors: Jon Atli Benediktsson, Mauro Dalla Mura, Lorenzo Bruzzone, Jocelyn Chanussot, Philippe Salembier, Silvia ValeroAbstract:The new generation of very high resolution sensors in airborne or satellite remote sensing open the door to countless new applications with a high societal impact. In order to bridge the gap between the potential offered by these new sensors and the needs of the end-users to actually face tomorrow's challenges, advanced image processing methods need to be designed. In this paper we discuss two of the most promising strategies aiming at a hierarchical description and analysis of remote sensing data, namely the Extended Attribute Profiles (EAP) and the Binary Partition Trees (BPT). The EAP computes for each pixel a vector of Attributes providing a local multiscale representation of the information and hence leading to a fine description of the local structures of the image. Using different Attributes allows to address different contexts or applications. The BPTs provide a complete hierarchical description of the image, from the pixels (the leaves) to larger regions as the merging process goes on. The pruning of the tree provides a partition of the image and can address various goals (segmentation, object extraction, classification). The EAP and BPT approaches are used in experiments and the obtained results demonstrate their importance.
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classification of hyperspectral images with Extended Attribute profiles and feature extraction techniques
International Geoscience and Remote Sensing Symposium, 2010Co-Authors: Mauro Dalla Mura, Jon Atli Benediktsson, Lorenzo BruzzoneAbstract:In this paper we investigate the combined use of morphological Attribute filters and feature extraction techniques for the classification of a high resolution hyperspectral image. In greater detail, we propose to model the spatial information with Extended Attribute Profiles computed on the hyperspectral data and to reduce the high dimensionality of the morphological features computed (which show a high degree of redundancy) with feature extraction techniques. The features extracted are analyzed by two classifiers. The experimental analysis was carried out on a high resolution hyperspectral image acquired by the airborne sensor ROSIS-03 on the University of Pavia, Italy. The obtained results compared to those obtained without feature reduction proved the importance of the application of a stage of feature extraction in the process.
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Extended profiles with morphological Attribute filters for the analysis of hyperspectral data
International Journal of Remote Sensing, 2010Co-Authors: Mauro Dalla Mura, Jon Atli Benediktsson, Bjorn Waske, Lorenzo BruzzoneAbstract:Extended Attribute profiles and Extended multi-Attribute profiles are presented for the analysis of hyperspectral high-resolution images. These Extended profiles are based on morphological Attribute filters and, through a multi-level analysis, are capable of extracting spatial features that can better model the spatial information, with respect to conventional Extended morphological profiles. The features extracted by the proposed Extended profiles were considered for a classification task. Two hyperspectral high-resolution datasets acquired for the city of Pavia, Italy, were considered in the analysis. The effectiveness of the introduced operators in modelling the spatial information was proved by the higher classification accuracies obtained with respect to those achieved by a conventional Extended morphological profile.
Prashanth Reddy Marpu - One of the best experts on this subject based on the ideXlab platform.
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automatic generation of standard deviation Attribute profiles for spectral spatial classification of remote sensing data
IEEE Geoscience and Remote Sensing Letters, 2013Co-Authors: Prashanth Reddy Marpu, Jon Atli Benediktsson, Mauro Dalla Mura, Mattia Pedergnana, Lorenzo BruzzoneAbstract:Extended Attribute profiles, which are based on Attribute filters, have recently been presented as efficient tools for spectral-spatial classification of remote sensing images. However, construction of these profiles usually requires manual selection of parameters for the corresponding Attribute filters. In this letter, we present a technique to automatically build the Extended Attribute profiles with the standard deviation Attribute based on the statistics of the samples belonging to the classes of interest. The methodology is tested on two widely used hyperspectral images and the results are found to be highly accurate.
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classification of hyperspectral data using Extended Attribute profiles based on supervised and unsupervised feature extraction techniques
International Journal of Image and Data Fusion, 2012Co-Authors: Prashanth Reddy Marpu, Jon Atli Benediktsson, Mauro Dalla Mura, Mattia Pedergnana, Stijn Peeters, Lorenzo BruzzoneAbstract:The classification of remote sensing data based on the exploitation of spatial features extracted with morphological and Attribute profiles has been recently gaining importance. With the development of efficient algorithms to construct the profiles for large datasets, such methods are becoming even more relevant. When dealing with hyperspectral imagery, the profiles are traditionally built on the first few principal components computed from the data. However, it needs to be determined if other feature reduction approaches are better suited to create base images for the profiles. In this article, we explore the use of profiles based on features derived from three supervised feature extraction techniques (i.e. Discriminant Analysis Feature Extraction, Decision Boundary Feature Extraction and Non-parametric Weighted Feature Extraction) and two unsupervised feature-extraction techniques (i.e. Principal Component Analysis (PCA) and Kernel PCA) in classification and compare the classification accuracies obtaine...
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classification of remote sensing optical and lidar data using Extended Attribute profiles
IEEE Journal of Selected Topics in Signal Processing, 2012Co-Authors: Mattia Pedergnana, Jon Atli Benediktsson, Prashanth Reddy Marpu, Dalla M Mura, Lorenzo BruzzoneAbstract:Extended Attribute Profiles (EAPs), which are obtained by applying morphological Attribute filters to an image in a multilevel architecture, can be used for the characterization of the spatial characteristics of objects in a scene. EAPs have proved to be discriminant features when considered for thematic classification in remote sensing applications especially when dealing with very high resolution images. Altimeter data (such as LiDAR) can provide important information, which being complementary to the spectral one can be valuable for a better characterization of the surveyed scene. In this paper, we propose a technique performing a classification of the features extracted with EAPs computed on both optical and LiDAR images, leading to a fusion of the spectral, spatial and elevation data. The experiments were carried out on LiDAR data along either with a hyperspectral and a multispectral image acquired on a rural and urban area of the city of Trento (Italy), respectively. The classification accuracies obtained pointed out the effectiveness of the features extracted by EAPs on both optical and LiDAR data for classification.
Mattia Pedergnana - One of the best experts on this subject based on the ideXlab platform.
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automatic generation of standard deviation Attribute profiles for spectral spatial classification of remote sensing data
IEEE Geoscience and Remote Sensing Letters, 2013Co-Authors: Prashanth Reddy Marpu, Jon Atli Benediktsson, Mauro Dalla Mura, Mattia Pedergnana, Lorenzo BruzzoneAbstract:Extended Attribute profiles, which are based on Attribute filters, have recently been presented as efficient tools for spectral-spatial classification of remote sensing images. However, construction of these profiles usually requires manual selection of parameters for the corresponding Attribute filters. In this letter, we present a technique to automatically build the Extended Attribute profiles with the standard deviation Attribute based on the statistics of the samples belonging to the classes of interest. The methodology is tested on two widely used hyperspectral images and the results are found to be highly accurate.
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classification of hyperspectral data using Extended Attribute profiles based on supervised and unsupervised feature extraction techniques
International Journal of Image and Data Fusion, 2012Co-Authors: Prashanth Reddy Marpu, Jon Atli Benediktsson, Mauro Dalla Mura, Mattia Pedergnana, Stijn Peeters, Lorenzo BruzzoneAbstract:The classification of remote sensing data based on the exploitation of spatial features extracted with morphological and Attribute profiles has been recently gaining importance. With the development of efficient algorithms to construct the profiles for large datasets, such methods are becoming even more relevant. When dealing with hyperspectral imagery, the profiles are traditionally built on the first few principal components computed from the data. However, it needs to be determined if other feature reduction approaches are better suited to create base images for the profiles. In this article, we explore the use of profiles based on features derived from three supervised feature extraction techniques (i.e. Discriminant Analysis Feature Extraction, Decision Boundary Feature Extraction and Non-parametric Weighted Feature Extraction) and two unsupervised feature-extraction techniques (i.e. Principal Component Analysis (PCA) and Kernel PCA) in classification and compare the classification accuracies obtaine...
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classification of remote sensing optical and lidar data using Extended Attribute profiles
IEEE Journal of Selected Topics in Signal Processing, 2012Co-Authors: Mattia Pedergnana, Jon Atli Benediktsson, Prashanth Reddy Marpu, Dalla M Mura, Lorenzo BruzzoneAbstract:Extended Attribute Profiles (EAPs), which are obtained by applying morphological Attribute filters to an image in a multilevel architecture, can be used for the characterization of the spatial characteristics of objects in a scene. EAPs have proved to be discriminant features when considered for thematic classification in remote sensing applications especially when dealing with very high resolution images. Altimeter data (such as LiDAR) can provide important information, which being complementary to the spectral one can be valuable for a better characterization of the surveyed scene. In this paper, we propose a technique performing a classification of the features extracted with EAPs computed on both optical and LiDAR images, leading to a fusion of the spectral, spatial and elevation data. The experiments were carried out on LiDAR data along either with a hyperspectral and a multispectral image acquired on a rural and urban area of the city of Trento (Italy), respectively. The classification accuracies obtained pointed out the effectiveness of the features extracted by EAPs on both optical and LiDAR data for classification.
