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Jean-yves Tourneret - One of the best experts on this subject based on the ideXlab platform.

  • UnMixing multitemporal hyperspectral images with variability: an online algorithm
    2016
    Co-Authors: Pierre-antoine Thouvenin, Nicolas Dobigeon, Jean-yves Tourneret
    Abstract:

    Hyperspectral unMixing consists in determining the reference spectral signatures composing a hyperspectral image and their relative abundance fractions in each pixel. In practice, the identified signatures may be affected by a significant spectral variability resulting for instance from the temporal evolution of the imaged scene. This phenomenon can be accounted for by using a perturbed Linear Mixing Model. This paper studies an online estimation algorithm for the parameters of this extended Linear Mixing Model. This algorithm is of interest for the practical applications where the size of the hyper-spectral images precludes the use of batch procedures. The performance of the proposed method is evaluated on synthetic data.

  • Hyperspectral UnMixing With Spectral Variability Using a Perturbed Linear Mixing Model
    IEEE Transactions on Signal Processing, 2016
    Co-Authors: Pierre-antoine Thouvenin, Nicolas Dobigeon, Jean-yves Tourneret
    Abstract:

    Given a mixed hyperspectral data set, Linear unMixing aims at estimating the reference spectral signatures composing the data—referred to as endmembers—their abundance fractions and their number. In practice, the identified endmembers can vary spectrally within a given image and can thus be construed as variable instances of reference endmembers. Ignoring this variability induces estimation errors that are propagated into the unMixing procedure. To address this issue, endmember variability estimation consists of estimating the reference spectral signatures from which the estimated endmembers have been derived as well as their variability with respect to these references. This paper introduces a new Linear Mixing Model that explicitly accounts for spatial and spectral endmember variabilities. The parameters of this Model can be estimated using an optimization algorithm based on the alternating direction method of multipliers. The performance of the proposed unMixing method is evaluated on synthetic and real data. A comparison with state-of-the-art algorithms designed to Model and estimate endmember variability allows the interest of the proposed unMixing solution to be appreciated.

  • ICASSP - UnMixing multitemporal hyperspectral images with variability: An online algorithm
    2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016
    Co-Authors: Pierre-antoine Thouvenin, Nicolas Dobigeon, Jean-yves Tourneret
    Abstract:

    Hyperspectral unMixing consists in determining the reference spectral signatures composing a hyperspectral image and their relative abundance fractions in each pixel. In practice, the identified signatures may be affected by a significant spectral variability resulting for instance from the temporal evolution of the imaged scene. This phenomenon can be accounted for by using a perturbed Linear Mixing Model. This paper studies an online estimation algorithm for the parameters of this extended Linear Mixing Model. This algorithm is of interest for the practical applications where the size of the hyper-spectral images precludes the use of batch procedures. The performance of the proposed method is evaluated on synthetic data.

  • A perturbed Linear Mixing Model accounting for spectral variability
    2015 23rd European Signal Processing Conference (EUSIPCO), 2015
    Co-Authors: Pierre-antoine Thouvenin, Nicolas Dobigeon, Jean-yves Tourneret
    Abstract:

    Hyperspectral unMixing aims at determining the reference spectral signatures composing a hyperspectral image, their abundance fractions and their number. In practice, the spectral variability of the identified signatures induces significant abundance estimation errors. To address this issue, this paper introduces a new Linear Mixing Model explicitly accounting for this phenomenon. In this setting, the extracted endmembers are interpreted as possibly corrupted versions of the true endmembers. The parameters of this Model can be estimated using an optimization algorithm based on the alternating direction method of multipliers. The performance of the proposed unMixing method is evaluated on synthetic and real data.

  • EUSIPCO - A perturbed Linear Mixing Model accounting for spectral variability
    2015 23rd European Signal Processing Conference (EUSIPCO), 2015
    Co-Authors: Pierre-antoine Thouvenin, Nicolas Dobigeon, Jean-yves Tourneret
    Abstract:

    Hyperspectral unMixing aims at determining the reference spectral signatures composing a hyperspectral image, their abundance fractions and their number. In practice, the spectral variability of the identified signatures induces significant abundance estimation errors. To address this issue, this paper introduces a new Linear Mixing Model explicitly accounting for this phenomenon. In this setting, the extracted endmembers are interpreted as possibly corrupted versions of the true endmembers. The parameters of this Model can be estimated using an optimization algorithm based on the alternating direction method of multipliers. The performance of the proposed unMixing method is evaluated on synthetic and real data.

Yosio Edemir Shimabukuro - One of the best experts on this subject based on the ideXlab platform.

  • residual information to estimate uncertainty and improve the spectral Linear Mixing Model solution
    International Geoscience and Remote Sensing Symposium, 2012
    Co-Authors: Daniel C Zanotta, V.f. Haertel, Yosio Edemir Shimabukuro, Camilo Daleles Renno
    Abstract:

    This paper proposes an analysis on the residual term resulting from the Linear Spectral Mixing Model (SLMM) solution in order to access Model uncertainty. The framework employed here is based on analysis of data produced initially by unMixing of vegetation, bare soil and shade/water, whose are commonly used as standard endmembers. We suggest procedures to identify missing components in the mixture problem and automatically compute the spectral endmember values for these components directly from image data and residual information. The techniques proposed have been tested on real TM-Landsat. The results obtained promises and confirm the validity of the proposed approach.

  • IGARSS - Residual information to estimate uncertainty and improve the spectral Linear Mixing Model solution
    2012 IEEE International Geoscience and Remote Sensing Symposium, 2012
    Co-Authors: Daniel C Zanotta, V.f. Haertel, Yosio Edemir Shimabukuro, Camilo Daleles Renno
    Abstract:

    This paper proposes an analysis on the residual term resulting from the Linear Spectral Mixing Model (SLMM) solution in order to access Model uncertainty. The framework employed here is based on analysis of data produced initially by unMixing of vegetation, bare soil and shade/water, whose are commonly used as standard endmembers. We suggest procedures to identify missing components in the mixture problem and automatically compute the spectral endmember values for these components directly from image data and residual information. The techniques proposed have been tested on real TM-Landsat. The results obtained promises and confirm the validity of the proposed approach.

  • Spectral Linear Mixing Model in low spatial resolution image data
    IEEE Transactions on Geoscience and Remote Sensing, 2005
    Co-Authors: V.f. Haertel, Yosio Edemir Shimabukuro
    Abstract:

    Different ways to estimate the spectral reflectance for the component classes in a mixture problem have been proposed in the literature (pure pixels, spectral library, field measurements). One of the most common approaches consists in the use of pure pixels, i.e., pixels that are covered by a single component class. This approach presents the advantage of allowing the extraction of the components' reflectance directly from the image data. This approach, however, is generally not feasible in the case of low spatial resolution image data, due to the large ground area covered by a single pixel. In this paper, a methodology aiming to overcome this limitation is proposed. The proposed approach makes use of the spectral Linear Mixing Model. In the proposed methodology, the components' proportions in image data are estimated using a medium spatial resolution image as auxiliary data. The Linear Mixing Model is then solved for the unknown spectral reflectances. Experiments are presented, using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus, as low and medium spatial resolution image data, respectively, acquired on the same date over the Tapajos study site, Brazilian Amazon. Three component classes or endmembers are present in the scene covered by the experiment, namely vegetation, exposed soil, and shade. The components' spectral reflectance for the Terra MODIS spectral bands were then estimated by applying the proposed methodology. The reliability of these estimates is appraised by analyzing scatter diagrams produced by the Terra MODIS spectral bands and also by comparing the fraction images produced using both image datasets. This methodology appears appropriate for up-scaling information for regional and global studies.

  • spectral Linear Mixing Model in low spatial resolution image data
    International Geoscience and Remote Sensing Symposium, 2004
    Co-Authors: V.f. Haertel, Yosio Edemir Shimabukuro
    Abstract:

    The aim of this study consists in proposing and testing a methodology to estimate the spectral reflectance of the component classes in the mixed pixel problem, for the case of low spatial resolution image data. The well known Linear Mixing Model is modified in order to estimate the components spectral reflectance. Terra-MODIS image data, with a pixel size of 500 m is used to test the results.

  • Linear Mixing Model applied to AVHRR LAC data
    1993
    Co-Authors: Brent N. Holben, Yosio Edemir Shimabukuro
    Abstract:

    A Linear Mixing Model was applied to coarse spatial resolution data from the NOAA Advanced Very High Resolution Radiometer. The reflective component of the 3.55 - 3.93 microns channel was extracted and used with the two reflective channels 0.58 - 0.68 microns and 0.725 - 1.1 microns to run a Constraine Least Squares Model to generate vegetation, soil, and shade fraction images for an area in the Western region of Brazil. The Landsat Thematic Mapper data covering the Emas National park region was used for estimating the spectral response of the mixture components and for evaluating the Mixing Model results. The fraction images were compared with an unsupervised classification derived from Landsat TM data acquired on the same day. The relationship between the fraction images and normalized difference vegetation index images show the potential of the unMixing techniques when using coarse resolution data for global studies.

Nicolas Dobigeon - One of the best experts on this subject based on the ideXlab platform.

  • UnMixing multitemporal hyperspectral images with variability: an online algorithm
    2016
    Co-Authors: Pierre-antoine Thouvenin, Nicolas Dobigeon, Jean-yves Tourneret
    Abstract:

    Hyperspectral unMixing consists in determining the reference spectral signatures composing a hyperspectral image and their relative abundance fractions in each pixel. In practice, the identified signatures may be affected by a significant spectral variability resulting for instance from the temporal evolution of the imaged scene. This phenomenon can be accounted for by using a perturbed Linear Mixing Model. This paper studies an online estimation algorithm for the parameters of this extended Linear Mixing Model. This algorithm is of interest for the practical applications where the size of the hyper-spectral images precludes the use of batch procedures. The performance of the proposed method is evaluated on synthetic data.

  • Hyperspectral UnMixing With Spectral Variability Using a Perturbed Linear Mixing Model
    IEEE Transactions on Signal Processing, 2016
    Co-Authors: Pierre-antoine Thouvenin, Nicolas Dobigeon, Jean-yves Tourneret
    Abstract:

    Given a mixed hyperspectral data set, Linear unMixing aims at estimating the reference spectral signatures composing the data—referred to as endmembers—their abundance fractions and their number. In practice, the identified endmembers can vary spectrally within a given image and can thus be construed as variable instances of reference endmembers. Ignoring this variability induces estimation errors that are propagated into the unMixing procedure. To address this issue, endmember variability estimation consists of estimating the reference spectral signatures from which the estimated endmembers have been derived as well as their variability with respect to these references. This paper introduces a new Linear Mixing Model that explicitly accounts for spatial and spectral endmember variabilities. The parameters of this Model can be estimated using an optimization algorithm based on the alternating direction method of multipliers. The performance of the proposed unMixing method is evaluated on synthetic and real data. A comparison with state-of-the-art algorithms designed to Model and estimate endmember variability allows the interest of the proposed unMixing solution to be appreciated.

  • ICASSP - UnMixing multitemporal hyperspectral images with variability: An online algorithm
    2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016
    Co-Authors: Pierre-antoine Thouvenin, Nicolas Dobigeon, Jean-yves Tourneret
    Abstract:

    Hyperspectral unMixing consists in determining the reference spectral signatures composing a hyperspectral image and their relative abundance fractions in each pixel. In practice, the identified signatures may be affected by a significant spectral variability resulting for instance from the temporal evolution of the imaged scene. This phenomenon can be accounted for by using a perturbed Linear Mixing Model. This paper studies an online estimation algorithm for the parameters of this extended Linear Mixing Model. This algorithm is of interest for the practical applications where the size of the hyper-spectral images precludes the use of batch procedures. The performance of the proposed method is evaluated on synthetic data.

  • A perturbed Linear Mixing Model accounting for spectral variability
    2015 23rd European Signal Processing Conference (EUSIPCO), 2015
    Co-Authors: Pierre-antoine Thouvenin, Nicolas Dobigeon, Jean-yves Tourneret
    Abstract:

    Hyperspectral unMixing aims at determining the reference spectral signatures composing a hyperspectral image, their abundance fractions and their number. In practice, the spectral variability of the identified signatures induces significant abundance estimation errors. To address this issue, this paper introduces a new Linear Mixing Model explicitly accounting for this phenomenon. In this setting, the extracted endmembers are interpreted as possibly corrupted versions of the true endmembers. The parameters of this Model can be estimated using an optimization algorithm based on the alternating direction method of multipliers. The performance of the proposed unMixing method is evaluated on synthetic and real data.

  • EUSIPCO - A perturbed Linear Mixing Model accounting for spectral variability
    2015 23rd European Signal Processing Conference (EUSIPCO), 2015
    Co-Authors: Pierre-antoine Thouvenin, Nicolas Dobigeon, Jean-yves Tourneret
    Abstract:

    Hyperspectral unMixing aims at determining the reference spectral signatures composing a hyperspectral image, their abundance fractions and their number. In practice, the spectral variability of the identified signatures induces significant abundance estimation errors. To address this issue, this paper introduces a new Linear Mixing Model explicitly accounting for this phenomenon. In this setting, the extracted endmembers are interpreted as possibly corrupted versions of the true endmembers. The parameters of this Model can be estimated using an optimization algorithm based on the alternating direction method of multipliers. The performance of the proposed unMixing method is evaluated on synthetic and real data.

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

  • spectral unMixing a derivation of the extended Linear Mixing Model from the hapke Model
    IEEE Geoscience and Remote Sensing Letters, 2020
    Co-Authors: Lucas Drumetz, Jocelyn Chanussot, Christian Jutten
    Abstract:

    In hyperspectral imaging, spectral unMixing aims at decomposing the image into a set of reference spectral signatures corresponding to the materials present in the observed scene and their relative proportions in every pixel. While a Linear Mixing Model was used for a long time, the complex nature of the physicochemical phenomena that affect the spectra of the materials led to shift the community’s attention toward algorithms accounting for the variability of the endmembers. Such intraclass variations are mainly due to local changes in the composition of the materials and to illumination changes. In the physical remote sensing community, a popular Model accounting for illumination variability is the radiative transfer Model proposed by Hapke. It is, however, too complex to be directly used in hyperspectral unMixing in a tractable way. Instead, the extended Linear Mixing Model (ELMM) allows to easily unmix the hyperspectral data accounting for changing illumination conditions and to address nonLinear effects to some extent. In this letter, we show that the ELMM can be obtained from the Hapke Model by successively simplifying physical assumptions, whose validity we experimentally examine, thus demonstrating its relevance to handle illumination-induced variability in the unMixing problem.

  • Spectral UnMixing: A Derivation of the Extended Linear Mixing Model from the Hapke Model
    IEEE Geoscience and Remote Sensing Letters, 2020
    Co-Authors: Lucas Drumetz, Jocelyn Chanussot, Christian Jutten
    Abstract:

    In hyperspectral imaging, spectral unMixing aims at decomposing the image into a set of reference spectral signatures corresponding to the materials present in the observed scene and their relative proportions in every pixel. While a Linear Mixing Model was used for a long time, the complex nature of the physico-chemical phenomena that affect the spectra of the materials led to shift the community's attention towards algorithms accounting for the variability of the endmembers. Such intra-class variations are mainly due to local changes in the composition of the materials, and to illumination changes. In the physical remote sensing community, a popular Model accounting for illumination variability is the radiative transfer Model proposed by Hapke. It is however too complex to be directly used in hyperspectral unMixing in a tractable way. Instead, the Extended Linear Mixing Model (ELMM) allows to easily unmix hyperspectral data accounting for changing illumination conditions and to address nonLinear effects to some extent. In this letter, we show that the ELMM can be obtained from the Hapke Model by successive simplifying physical assumptions, whose validity we experimentally examine, thus demonstrating its relevance to handle illumination induced variability in the unMixing problem. Index Terms-Hyperspectral image unMixing, spectral variability , Hapke Model, extended Linear Mixing Model.

  • Spectral Variability Aware Blind Hyperspectral Image UnMixing Based on Convex Geometry
    IEEE Transactions on Image Processing, 2020
    Co-Authors: Lucas Drumetz, Jocelyn Chanussot, Christian Jutten, Akira Iwasaki
    Abstract:

    Hyperspectral image unMixing has proven to be a useful technique to interpret hyperspectral data, and is a prolific research topic in the community. Most of the approaches used to perform Linear unMixing are based on convex geometry concepts, because of the strong geometrical structure of the Linear Mixing Model. However, many algorithms based on convex geometry are still used in spite of the underlying Model not considering the intra-class variability of the materials. A natural question is to wonder to what extent these concepts and tools (Intrinsic Dimensionality estimation, endmember extraction algorithms, pixel purity) are still relevant when spectral variability comes into play. In this paper, we first analyze their robustness in a case where the Linear Mixing Model holds in each pixel, but the endmembers vary in each pixel according to a prescribed variability Model. In the light of this analysis, we propose an integrated unMixing chain which tries to adress the shortcomings of the classical tools used in the Linear case, based on our previously proposed extended Linear Mixing Model. We show the interest of the proposed approach on simulated and real datasets.

  • Spectral UnMixing: A Derivation of the Extended Linear Mixing Model from the Hapke Model
    arXiv: Image and Video Processing, 2019
    Co-Authors: Lucas Drumetz, Jocelyn Chanussot, Christian Jutten
    Abstract:

    In hyperspectral imaging, spectral unMixing aims at decomposing the image into a set of reference spectral signatures corresponding to the materials present in the observed scene and their relative proportions in every pixel. While a Linear Mixing Model was used for a long time, the complex nature of the physical Mixing processes, led to shift the community's attention towards nonLinear Models and algorithms accounting for the variability of the endmembers. Such intra class variations are due to local changes in the physico-chemical composition of the materials, and to illumination changes. In the physical remote sensing community, a popular Model accounting for illumination variability is the radiative transfer Model proposed by Hapke. It is however too complex to be directly used in hyperspectral unMixing in a tractable way. Instead, the Extended Linear Mixing Model (ELMM) allows to easily unmix hyperspectral data accounting for changing illumination conditions. In this letter, we show that the ELMM can be obtained from the Hapke Model by successive simplifiying physical assumptions, thus theoretically confirming its relevance to handle illumination induced variability in the unMixing problem.

  • An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral UnMixing
    IEEE Transactions on Image Processing, 2019
    Co-Authors: Danfeng Hong, Jocelyn Chanussot, Naoto Yokoya, Xiao Xiang Zhu
    Abstract:

    Hyperspectral imagery collected from airborne or satellite sources inevitably suffers from spectral variability, making it difficult for spectral unMixing to accurately estimate abundance maps. The classical unMixing Model, the Linear Mixing Model (LMM), generally fails to handle this sticky issue effectively. To this end, we propose a novel spectral mixture Model, called the augmented LMM, to address spectral variability by applying a data-driven learning strategy in inverse problems of hyperspectral unMixing. The proposed approach Models the main spectral variability (i.e., scaling factors) generated by variations in illumination or typography separately by means of the endmember dictionary. It then Models other spectral variabilities caused by environmental conditions (e.g., local temperature and humidity and atmospheric effects) and instrumental configurations (e.g., sensor noise), and material nonLinear Mixing effects, by introducing a spectral variability dictionary. To effectively run the data-driven learning strategy, we also propose a reasonable prior knowledge for the spectral variability dictionary, whose atoms are assumed to be low-coherent with spectral signatures of endmembers, which leads to a well-known low-coherence dictionary learning problem. Thus, a dictionary learning technique is embedded in the framework of spectral unMixing so that the algorithm can learn the spectral variability dictionary and estimate the abundance maps simultaneously. Extensive experiments on synthetic and real datasets are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with the previous state-of-the-art methods.

Ping Huang - One of the best experts on this subject based on the ideXlab platform.

  • coupling a two tip Linear Mixing Model with a δd δ18o plot to determine water sources consumed by maize during different growth stages
    Field Crops Research, 2011
    Co-Authors: Congzhi Zhang, Jiabao Zhang, Bingzi Zhao, Anning Zhu, Hui Zhang, Ping Huang
    Abstract:

    Abstract Linear Mixing Models and δ D– δ 18 O plots with stable hydrogen and oxygen isotopes have been widely used to identify water sources consumed by various plants; however, each of these methods is incapable of quantifying the contribution of different water sources used if more than three sources exist simultaneously. In this study, we developed a coupled Model to solve this problem and applied it to determine the contribution of various water sources to maize during different growth stages. A field experiment was conducted from June 5 to September 12, 2007. The results revealed that primary water sources for maize varied with growth stages, and that generally more water from deeper depths was used as the plants grew. Additionally, calculation of the coupled Model was in accordance with the Linear Mixing Model, which indicated that the coupled Model could enable the successful identification of various water sources that contribute the total water used by plants.

  • Coupling a two-tip Linear Mixing Model with a δD–δ18O plot to determine water sources consumed by maize during different growth stages
    Field Crops Research, 2011
    Co-Authors: Congzhi Zhang, Jiabao Zhang, Bingzi Zhao, Anning Zhu, Hui Zhang, Ping Huang
    Abstract:

    Abstract Linear Mixing Models and δ D– δ 18 O plots with stable hydrogen and oxygen isotopes have been widely used to identify water sources consumed by various plants; however, each of these methods is incapable of quantifying the contribution of different water sources used if more than three sources exist simultaneously. In this study, we developed a coupled Model to solve this problem and applied it to determine the contribution of various water sources to maize during different growth stages. A field experiment was conducted from June 5 to September 12, 2007. The results revealed that primary water sources for maize varied with growth stages, and that generally more water from deeper depths was used as the plants grew. Additionally, calculation of the coupled Model was in accordance with the Linear Mixing Model, which indicated that the coupled Model could enable the successful identification of various water sources that contribute the total water used by plants.