The Experts below are selected from a list of 14868 Experts worldwide ranked by ideXlab platform

Philippe Salembier - One of the best experts on this subject based on the ideXlab platform.

  • study of binary Partition Tree pruning techniques for polarimetric sar images
    International Symposium on Memory Management, 2015
    Co-Authors: Philippe Salembier
    Abstract:

    This paper investigates several pruning techniques applied on Binary Partition Trees (BPTs) and their usefulness for low-level processing of PolSAR images. BPTs group pixels to form homogeneous regions, which are hierarchically structured by inclusion in a binary Tree. They provide multiple resolutions of description and easy access to subsets of regions. Once constructed, BPTs can be used for a large number of applications. Many of these applications consist in populating the Tree with a specific feature and in applying a graph-cut called pruning to extract a Partition of the space. In this paper, different pruning examples involving the optimization of a global criterion are discussed and analyzed in the context of PolSAR images for segmentation. Initial experiments are also reported on the use of Minkowski norms in the definition of the optimization criterion.

  • ISMM - Study of Binary Partition Tree Pruning Techniques for Polarimetric SAR Images
    Lecture Notes in Computer Science, 2015
    Co-Authors: Philippe Salembier
    Abstract:

    This paper investigates several pruning techniques applied on Binary Partition Trees (BPTs) and their usefulness for low-level processing of PolSAR images. BPTs group pixels to form homogeneous regions, which are hierarchically structured by inclusion in a binary Tree. They provide multiple resolutions of description and easy access to subsets of regions. Once constructed, BPTs can be used for a large number of applications. Many of these applications consist in populating the Tree with a specific feature and in applying a graph-cut called pruning to extract a Partition of the space. In this paper, different pruning examples involving the optimization of a global criterion are discussed and analyzed in the context of PolSAR images for segmentation. Initial experiments are also reported on the use of Minkowski norms in the definition of the optimization criterion.

  • Object recognition in hyperspectral images using Binary Partition Tree representation
    Pattern Recognition Letters, 2015
    Co-Authors: Silvia Valero, Philippe Salembier, Jocelyn Chanussot
    Abstract:

    New object detection technique by using hierarchical region-based image representations.Binary Partition Tree is proposed as a structured search space in order to incorporate the spectral and the spatial information.The strategy is applied on several datasets of hyperspectral images of urban areas.The obtained results show the interest of studying the objects of the scene with a region-based perspective. In this work, an image representation based on Binary Partition Tree is proposed for object detection in hyperspectral images. This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a Tree structure, which succeeds in presenting: (i) the decomposition of the image in terms of coherent regions and (ii) the inclusion relations of the regions in the scene. Hence, the BPT representation defines a search space for constructing a robust object identification scheme. Spatial and spectral information are integrated in order to analyze hyperspectral images with a region-based perspective. For each region represented in the BPT, spatial and spectral descriptors are computed and the likelihood that they correspond to an instantiation of the object of interest is evaluated. Experimental results demonstrate the good performances of this BPT-based approach.

  • multidimensional sar data analysis based on binary Partition Trees and the covariance matrix geometry
    International Radar Conference, 2014
    Co-Authors: Alberto Alonsogonzalez, Philippe Salembier, Silvia Valero, Carlos Lopezmartinez, Jocelyn Chanussot
    Abstract:

    In this paper, we propose the use of the Binary Partition Tree (BPT) as a region-based and multi-scale image representation to process multidimensional SAR data, with special emphasis on polarimetric SAR data. We also show that this approach could be extended to other types of remote sensing imaging technologies, such as hyperspatial imagery. The Binary Partition Tree contains a lot of information about the image structure at different detail levels. At the same time, this structure represents a convenient vehicle to exploit both the statistical properties, as well as the geometric properties of the multidimensional SAR data given by the covariance matrix. The BPT construction process and its exploitation for PolSAR and temporal data information estimation is analyzed in this work. In particular, this work focuses on the speckle noise filtering problem and the temporal characterization of the image dynamics. Results with real data are presented to illustrate the capabilities of the BPT processing approach, specially to maintain the spatial resolution and the small details of the image.

  • object recognition in urban hyperspectral images using binary Partition Tree representation
    International Geoscience and Remote Sensing Symposium, 2013
    Co-Authors: Silvia Valero, Philippe Salembier, Jocelyn Chanussot
    Abstract:

    In this work, an image representation based on Binary Partition Tree is proposed for object detection in hyperspectral images. The BPT representation defines a search space for constructing a robust object identification scheme. Spatial and spectral information are integrated in order to analyze hyperspectral images with a region-based perspective. Experimental results demonstrate the good performances of this BPT-based approach.

Jocelyn Chanussot - One of the best experts on this subject based on the ideXlab platform.

  • Object recognition in hyperspectral images using Binary Partition Tree representation
    Pattern Recognition Letters, 2015
    Co-Authors: Silvia Valero, Philippe Salembier, Jocelyn Chanussot
    Abstract:

    New object detection technique by using hierarchical region-based image representations.Binary Partition Tree is proposed as a structured search space in order to incorporate the spectral and the spatial information.The strategy is applied on several datasets of hyperspectral images of urban areas.The obtained results show the interest of studying the objects of the scene with a region-based perspective. In this work, an image representation based on Binary Partition Tree is proposed for object detection in hyperspectral images. This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a Tree structure, which succeeds in presenting: (i) the decomposition of the image in terms of coherent regions and (ii) the inclusion relations of the regions in the scene. Hence, the BPT representation defines a search space for constructing a robust object identification scheme. Spatial and spectral information are integrated in order to analyze hyperspectral images with a region-based perspective. For each region represented in the BPT, spatial and spectral descriptors are computed and the likelihood that they correspond to an instantiation of the object of interest is evaluated. Experimental results demonstrate the good performances of this BPT-based approach.

  • Context-adaptive Pansharpening based on binary Partition Tree segmentation
    2014
    Co-Authors: Mauro Dalla Mura, G Vivone, R. Restaino, Jocelyn Chanussot
    Abstract:

    Pansharpening is a successful application of data fusion to remotely sensed data. It aims at obtaining a detailed representation of an Earth's zone both in terms of spatial and spectral resolution. This is done through the fusion of a panchromatic and a multispectral image (having complementary spatial and spectral resolutions) that are acquired simultaneously by several optical satellites. The result of the fusion is commonly achieved by introducing the spatial details, modulated opportunely by gains, in the multispectral one. The injection gains can be estimated globally over the image, or locally, thus obtaining spatially variant values. The latter approach has been proven to achieve better results and it is based on windowing the analyzed image in squared blocks. In this paper we propose a more elaborated concept of locality, as it is based on an opportune segmentation of the target scene. In greater details, we propose to estimate the local injection gains on regions composed of pixel with similar spectral characteristic, as defined by a segmentation. Such local approach is compared to the global one and to the conventional local estimation based on overlapping and non-overlapping blocks. The performances have been assessed by using three real datasets, the first acquired by WorldView-2 and the other two by Pléiades. The analysis evidences the appreciable improvements of the performances with respect to classical schemes.

  • multidimensional sar data analysis based on binary Partition Trees and the covariance matrix geometry
    International Radar Conference, 2014
    Co-Authors: Alberto Alonsogonzalez, Philippe Salembier, Silvia Valero, Carlos Lopezmartinez, Jocelyn Chanussot
    Abstract:

    In this paper, we propose the use of the Binary Partition Tree (BPT) as a region-based and multi-scale image representation to process multidimensional SAR data, with special emphasis on polarimetric SAR data. We also show that this approach could be extended to other types of remote sensing imaging technologies, such as hyperspatial imagery. The Binary Partition Tree contains a lot of information about the image structure at different detail levels. At the same time, this structure represents a convenient vehicle to exploit both the statistical properties, as well as the geometric properties of the multidimensional SAR data given by the covariance matrix. The BPT construction process and its exploitation for PolSAR and temporal data information estimation is analyzed in this work. In particular, this work focuses on the speckle noise filtering problem and the temporal characterization of the image dynamics. Results with real data are presented to illustrate the capabilities of the BPT processing approach, specially to maintain the spatial resolution and the small details of the image.

  • context adaptive pansharpening based on binary Partition Tree segmentation
    International Conference on Image Processing, 2014
    Co-Authors: Mauro Dalla Mura, R. Restaino, G Vivone, Jocelyn Chanussot
    Abstract:

    Pansharpening is a successful application of data fusion to remotely sensed data. It aims at obtaining a detailed representation of an Earth's zone both in terms of spatial and spectral resolution. This is done through the fusion of a panchromatic and a multispectral image (having complementary spatial and spectral resolutions) that are acquired simultaneously by several optical satellites. The result of the fusion is commonly achieved by introducing the spatial details, modulated opportunely by gains, in the multispectral one. The injection gains can be estimated globally over the image, or locally, thus obtaining spatially variant values. The latter approach has been proven to achieve better results and it is based on windowing the analyzed image in squared blocks. In this paper we propose a more elaborated concept of locality, as it is based on an opportune segmentation of the target scene. In greater details, we propose to estimate the local injection gains on regions composed of pixel with similar spectral characteristic, as defined by a segmentation. Such local approach is compared to the global one and to the conventional local estimation based on overlapping and non-overlapping blocks. The performances have been assessed by using three real datasets, the first acquired by WorldView-2 and the other two by Pleiades. The analysis evidences the appreciable improvements of the performances with respect to classical schemes.

  • Hyperspectral Image Segmentation Using a New Spectral Unmixing-Based Binary Partition Tree Representation
    IEEE Transactions on Image Processing, 2014
    Co-Authors: Miguel Angel Veganzones, Guillaume Tochon, Mauro Dalla Mura, Antonio J. Plaza, Jocelyn Chanussot
    Abstract:

    The Binary Partition Tree (BPT) is a hierarchical region-based representation of an image in a Tree structure. BPT allows users to explore the image at different segmentation scales. Often, the Tree is pruned to get a more compact representation and so the remaining nodes conform an optimal Partition for some given task. Here, we propose a novel BPT construction approach and pruning strategy for hyperspectral images based on spectral unmixing concepts. Linear Spectral Unmixing (LSU) consists of finding the spectral signatures of the materials present in the image (endmembers) and their fractional abundances within each pixel. The proposed methodology exploits the local unmixing of the regions to find the Partition achieving a global minimum reconstruction error. Results are presented on real hyperspectral data sets with different contexts and resolutions.

Miguel Angel Veganzones - One of the best experts on this subject based on the ideXlab platform.

  • Hyperspectral Image Segmentation Using a New Spectral Unmixing-Based Binary Partition Tree Representation
    IEEE Transactions on Image Processing, 2014
    Co-Authors: Miguel Angel Veganzones, Guillaume Tochon, Mauro Dalla Mura, Antonio J. Plaza, Jocelyn Chanussot
    Abstract:

    The Binary Partition Tree (BPT) is a hierarchical region-based representation of an image in a Tree structure. BPT allows users to explore the image at different segmentation scales. Often, the Tree is pruned to get a more compact representation and so the remaining nodes conform an optimal Partition for some given task. Here, we propose a novel BPT construction approach and pruning strategy for hyperspectral images based on spectral unmixing concepts. Linear Spectral Unmixing (LSU) consists of finding the spectral signatures of the materials present in the image (endmembers) and their fractional abundances within each pixel. The proposed methodology exploits the local unmixing of the regions to find the Partition achieving a global minimum reconstruction error. Results are presented on real hyperspectral data sets with different contexts and resolutions.

  • Binary Partition Tree-based local spectral unmixing
    2014
    Co-Authors: Lucas Drumetz, Antonio Plaza, Miguel Angel Veganzones, Ruben Marrero, Guillaume Tochon, Mauro Dalla Mura, Jocelyn Chanussot
    Abstract:

    The linear mixing model (LMM) is a widely used methodology for the spectral unmixing (SU) of hyperspectral data. In this model, hyperspectral data is formed as a linear combination of spectral signatures corresponding to macroscopically pure materials (endmembers), weighted by their fractional abundances. Some of the drawbacks of the LMM are the presence of multiple mixtures and the spectral variability of the endmembers due to illumination and atmospheric effects. These issues appear as variations of the spectral conditions of the image along its spatial domain. However, these effects are not so severe locally and could be at least mitigated by working in smaller regions of the image. The proposed local SU works over a Partition of the image, performing the spectral unmixing locally in each region of the Partition. In this work, we first introduce the general local SU methodology, then we propose an implementation of the local SU based on a binary Partition Tree representation of the hyperspectral image and finally we give an experimental validation of the approach using real data.

  • hyperspectral image segmentation using a new spectral unmixing based binary Partition Tree representation
    IEEE Transactions on Image Processing, 2014
    Co-Authors: Miguel Angel Veganzones, Antonio Plaza, Guillaume Tochon, Mauro Dallamura, Jocelyn Chanussot
    Abstract:

    The binary Partition Tree (BPT) is a hierarchical region-based representation of an image in a Tree structure. The BPT allows users to explore the image at different segmentation scales. Often, the Tree is pruned to get a more compact representation and so the remaining nodes conform an optimal Partition for some given task. Here, we propose a novel BPT construction approach and pruning strategy for hyperspectral images based on spectral unmixing concepts. Linear spectral unmixing consists of finding the spectral signatures of the materials present in the image (endmembers) and their fractional abundances within each pixel. The proposed methodology exploits the local unmixing of the regions to find the Partition achieving a global minimum reconstruction error. Results are presented on real hyperspectral data sets with different contexts and resolutions.

  • Hyperspectral image segmentation using a new spectral mixture-based binary Partition Tree representation
    2013
    Co-Authors: Miguel Angel Veganzones, Antonio Plaza, Guillaume Tochon, Mauro Dalla Mura, Jocelyn Chanussot
    Abstract:

    The Binary Partition Tree (BPT) is a hierarchical region-based representation of an image in a Tree structure. BPT allows users to explore the image at different segmentation scales, from fine Partitions close to the leaves to coarser Partitions close to the root. Often, the Tree is pruned so the leaves of the resulting pruned Tree conform an optimal Partition given some optimality criterion. Here, we propose a novel BPT construction approach and pruning strategy for hyperspectral images based on spectral unmixing concepts. The proposed methodology exploits the local unmixing of the regions to find the Partition achieving a global minimum reconstruction error. We successfully tested the proposed approach on the well-known Cuprite hyperspectral image collected by NASA Jet Propulsion Laboratory's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). This scene is considered as a standard benchmark to validate spectral unmixing algorithms.

  • ICIP - Hyperspectral image segmentation using a new spectral mixture-based binary Partition Tree representation
    2013 IEEE International Conference on Image Processing, 2013
    Co-Authors: Miguel Angel Veganzones, Antonio Plaza, Guillaume Tochon, M. Dalla Mura, Jocelyn Chanussot
    Abstract:

    The Binary Partition Tree (BPT) is a hierarchical region-based representation of an image in a Tree structure. BPT allows users to explore the image at different segmentation scales, from fine Partitions close to the leaves to coarser Partitions close to the root. Often, the Tree is pruned so the leaves of the resulting pruned Tree conform an optimal Partition given some optimality criterion. Here, we propose a novel BPT construction approach and pruning strategy for hyperspectral images based on spectral unmixing concepts. The proposed methodology exploits the local unmixing of the regions to find the Partition achieving a global minimum reconstruction error. We successfully tested the proposed approach on the well-known Cuprite hyperspectral image collected by NASA Jet Propulsion Laboratory's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). This scene is considered as a standard benchmark to validate spectral unmixing algorithms.

Silvia Valero - One of the best experts on this subject based on the ideXlab platform.

  • Object recognition in hyperspectral images using Binary Partition Tree representation
    Pattern Recognition Letters, 2015
    Co-Authors: Silvia Valero, Philippe Salembier, Jocelyn Chanussot
    Abstract:

    New object detection technique by using hierarchical region-based image representations.Binary Partition Tree is proposed as a structured search space in order to incorporate the spectral and the spatial information.The strategy is applied on several datasets of hyperspectral images of urban areas.The obtained results show the interest of studying the objects of the scene with a region-based perspective. In this work, an image representation based on Binary Partition Tree is proposed for object detection in hyperspectral images. This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a Tree structure, which succeeds in presenting: (i) the decomposition of the image in terms of coherent regions and (ii) the inclusion relations of the regions in the scene. Hence, the BPT representation defines a search space for constructing a robust object identification scheme. Spatial and spectral information are integrated in order to analyze hyperspectral images with a region-based perspective. For each region represented in the BPT, spatial and spectral descriptors are computed and the likelihood that they correspond to an instantiation of the object of interest is evaluated. Experimental results demonstrate the good performances of this BPT-based approach.

  • multidimensional sar data analysis based on binary Partition Trees and the covariance matrix geometry
    International Radar Conference, 2014
    Co-Authors: Alberto Alonsogonzalez, Philippe Salembier, Silvia Valero, Carlos Lopezmartinez, Jocelyn Chanussot
    Abstract:

    In this paper, we propose the use of the Binary Partition Tree (BPT) as a region-based and multi-scale image representation to process multidimensional SAR data, with special emphasis on polarimetric SAR data. We also show that this approach could be extended to other types of remote sensing imaging technologies, such as hyperspatial imagery. The Binary Partition Tree contains a lot of information about the image structure at different detail levels. At the same time, this structure represents a convenient vehicle to exploit both the statistical properties, as well as the geometric properties of the multidimensional SAR data given by the covariance matrix. The BPT construction process and its exploitation for PolSAR and temporal data information estimation is analyzed in this work. In particular, this work focuses on the speckle noise filtering problem and the temporal characterization of the image dynamics. Results with real data are presented to illustrate the capabilities of the BPT processing approach, specially to maintain the spatial resolution and the small details of the image.

  • object recognition in urban hyperspectral images using binary Partition Tree representation
    International Geoscience and Remote Sensing Symposium, 2013
    Co-Authors: Silvia Valero, Philippe Salembier, Jocelyn Chanussot
    Abstract:

    In this work, an image representation based on Binary Partition Tree is proposed for object detection in hyperspectral images. The BPT representation defines a search space for constructing a robust object identification scheme. Spatial and spectral information are integrated in order to analyze hyperspectral images with a region-based perspective. Experimental results demonstrate the good performances of this BPT-based approach.

  • Processing Multidimensional SAR and Hyperspectral Images With Binary Partition Tree
    Proceedings of the IEEE, 2013
    Co-Authors: Alberto Alonso-gonzalez, Silvia Valero, Jocelyn Chanussot, Carlos Lopez-martinez, Philippe Salembier
    Abstract:

    The current increase of spatial as well as spectral resolutions of modern remote sensing sensors represents a real opportunity for many practical applications but also generates important challenges in terms of image processing. In particular, the spatial correlation between pixels and/or the spectral correlation between spectral bands of a given pixel cannot be ignored. The traditional pixel-based representation of images does not facilitate the handling of these correlations. In this paper, we discuss the interest of a particular hierarchical region-based representation of images based on binary Partition Tree (BPT). This representation approach is very flexible as it can be applied to any type of image. Here both optical and radar images will be discussed. Moreover, once the image representation is computed, it can be used for many different applications. Filtering, segmentation, and classification will be detailed in this paper. In all cases, the interest of the BPT representation over the classical pixel-based representation will be highlighted.

  • IGARSS - Object recognition in urban hyperspectral images using Binary Partition Tree representation
    2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2013
    Co-Authors: Silvia Valero, Philippe Salembier, Jocelyn Chanussot
    Abstract:

    In this work, an image representation based on Binary Partition Tree is proposed for object detection in hyperspectral images. The BPT representation defines a search space for constructing a robust object identification scheme. Spatial and spectral information are integrated in order to analyze hyperspectral images with a region-based perspective. Experimental results demonstrate the good performances of this BPT-based approach.

Guillaume Tochon - One of the best experts on this subject based on the ideXlab platform.

  • Hyperspectral Image Segmentation Using a New Spectral Unmixing-Based Binary Partition Tree Representation
    IEEE Transactions on Image Processing, 2014
    Co-Authors: Miguel Angel Veganzones, Guillaume Tochon, Mauro Dalla Mura, Antonio J. Plaza, Jocelyn Chanussot
    Abstract:

    The Binary Partition Tree (BPT) is a hierarchical region-based representation of an image in a Tree structure. BPT allows users to explore the image at different segmentation scales. Often, the Tree is pruned to get a more compact representation and so the remaining nodes conform an optimal Partition for some given task. Here, we propose a novel BPT construction approach and pruning strategy for hyperspectral images based on spectral unmixing concepts. Linear Spectral Unmixing (LSU) consists of finding the spectral signatures of the materials present in the image (endmembers) and their fractional abundances within each pixel. The proposed methodology exploits the local unmixing of the regions to find the Partition achieving a global minimum reconstruction error. Results are presented on real hyperspectral data sets with different contexts and resolutions.

  • Binary Partition Tree-based local spectral unmixing
    2014
    Co-Authors: Lucas Drumetz, Antonio Plaza, Miguel Angel Veganzones, Ruben Marrero, Guillaume Tochon, Mauro Dalla Mura, Jocelyn Chanussot
    Abstract:

    The linear mixing model (LMM) is a widely used methodology for the spectral unmixing (SU) of hyperspectral data. In this model, hyperspectral data is formed as a linear combination of spectral signatures corresponding to macroscopically pure materials (endmembers), weighted by their fractional abundances. Some of the drawbacks of the LMM are the presence of multiple mixtures and the spectral variability of the endmembers due to illumination and atmospheric effects. These issues appear as variations of the spectral conditions of the image along its spatial domain. However, these effects are not so severe locally and could be at least mitigated by working in smaller regions of the image. The proposed local SU works over a Partition of the image, performing the spectral unmixing locally in each region of the Partition. In this work, we first introduce the general local SU methodology, then we propose an implementation of the local SU based on a binary Partition Tree representation of the hyperspectral image and finally we give an experimental validation of the approach using real data.

  • hyperspectral image segmentation using a new spectral unmixing based binary Partition Tree representation
    IEEE Transactions on Image Processing, 2014
    Co-Authors: Miguel Angel Veganzones, Antonio Plaza, Guillaume Tochon, Mauro Dallamura, Jocelyn Chanussot
    Abstract:

    The binary Partition Tree (BPT) is a hierarchical region-based representation of an image in a Tree structure. The BPT allows users to explore the image at different segmentation scales. Often, the Tree is pruned to get a more compact representation and so the remaining nodes conform an optimal Partition for some given task. Here, we propose a novel BPT construction approach and pruning strategy for hyperspectral images based on spectral unmixing concepts. Linear spectral unmixing consists of finding the spectral signatures of the materials present in the image (endmembers) and their fractional abundances within each pixel. The proposed methodology exploits the local unmixing of the regions to find the Partition achieving a global minimum reconstruction error. Results are presented on real hyperspectral data sets with different contexts and resolutions.

  • Hyperspectral image segmentation using a new spectral mixture-based binary Partition Tree representation
    2013
    Co-Authors: Miguel Angel Veganzones, Antonio Plaza, Guillaume Tochon, Mauro Dalla Mura, Jocelyn Chanussot
    Abstract:

    The Binary Partition Tree (BPT) is a hierarchical region-based representation of an image in a Tree structure. BPT allows users to explore the image at different segmentation scales, from fine Partitions close to the leaves to coarser Partitions close to the root. Often, the Tree is pruned so the leaves of the resulting pruned Tree conform an optimal Partition given some optimality criterion. Here, we propose a novel BPT construction approach and pruning strategy for hyperspectral images based on spectral unmixing concepts. The proposed methodology exploits the local unmixing of the regions to find the Partition achieving a global minimum reconstruction error. We successfully tested the proposed approach on the well-known Cuprite hyperspectral image collected by NASA Jet Propulsion Laboratory's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). This scene is considered as a standard benchmark to validate spectral unmixing algorithms.

  • ICIP - Hyperspectral image segmentation using a new spectral mixture-based binary Partition Tree representation
    2013 IEEE International Conference on Image Processing, 2013
    Co-Authors: Miguel Angel Veganzones, Antonio Plaza, Guillaume Tochon, M. Dalla Mura, Jocelyn Chanussot
    Abstract:

    The Binary Partition Tree (BPT) is a hierarchical region-based representation of an image in a Tree structure. BPT allows users to explore the image at different segmentation scales, from fine Partitions close to the leaves to coarser Partitions close to the root. Often, the Tree is pruned so the leaves of the resulting pruned Tree conform an optimal Partition given some optimality criterion. Here, we propose a novel BPT construction approach and pruning strategy for hyperspectral images based on spectral unmixing concepts. The proposed methodology exploits the local unmixing of the regions to find the Partition achieving a global minimum reconstruction error. We successfully tested the proposed approach on the well-known Cuprite hyperspectral image collected by NASA Jet Propulsion Laboratory's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). This scene is considered as a standard benchmark to validate spectral unmixing algorithms.