Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review

The spectral signatures of the materials contained in hyperspectral images (HI), also called endmembers (EM), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an HI. Traditional spectral unmixing (SU) algorithms neglect the spectral variability of the endmembers, what propagates significant mismodeling errors throughout the whole unmixing process and compromises the quality of its results. Therefore, large efforts have been recently dedicated to mitigate the effects of spectral variability in SU. This resulted in the development of algorithms that incorporate different strategies to allow the EMs to vary within an HI, using, for instance, sets of spectral signatures known a priori, Bayesian, parametric, or local EM models. Each of these approaches has different characteristics and underlying motivations. This paper presents a comprehensive literature review contextualizing both classic and recent approaches to solve this problem. We give a detailed evaluation of the sources of spectral variability and their effect in HI spectra. Furthermore, we propose a new taxonomy that organizes existing work according to a practitioner's point of view, based on the necessary amount of supervision and on the computational cost they require. We also review methods used to construct spectral libraries (which are required by many SU techniques) based on the observed HI, as well as algorithms for library augmentation and reduction. Finally, we conclude the paper with some discussions and an outline of possible future directions for the field.

[1]  Maria Petrou,et al.  Illumination invariant unmixing of sets of mixed pixels , 2001, IEEE Trans. Geosci. Remote. Sens..

[2]  Jilu Feng,et al.  The topographic normalization of hyperspectral data: implications for the selection of spectral end members and lithologic mapping , 2003 .

[3]  D. Roberts,et al.  Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE , 2003 .

[4]  Ricardo Augusto Borsoi,et al.  Deep Generative Endmember Modeling: An Application to Unsupervised Spectral Unmixing , 2019, IEEE Transactions on Computational Imaging.

[5]  Hannes Kazianka,et al.  A Bayesian approach to estimating linear mixtures with unknown covariance structure , 2011 .

[6]  Gregory Asner,et al.  Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis , 2000, IEEE Trans. Geosci. Remote. Sens..

[7]  Y. Kaufman,et al.  Aerosol climatology using a tunable spectral variability cloud screening of AERONET data , 2006 .

[8]  C. Biradar,et al.  Characterization of spatial variability of soil physicochemical properties and its impact on Rhodes grass productivity , 2016, Saudi journal of biological sciences.

[9]  F. Amiri,et al.  A sparsity-based Bayesian approach for hyperspectral unmixing using normal compositional model , 2018, Signal Image Video Process..

[10]  Caiyun Zhang,et al.  Multiscale quantification of urban composition from EO-1/Hyperion data using object-based spectral unmixing , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[11]  Jin Chen,et al.  Generalization of Subpixel Analysis for Hyperspectral Data With Flexibility in Spectral Similarity Measures , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Chein-I Chang,et al.  Finding endmember classes in hyperspectral imagery , 2015, Commercial + Scientific Sensing and Imaging.

[13]  Jocelyn Chanussot,et al.  Spectral Variability Aware Blind Hyperspectral Image Unmixing Based on Convex Geometry , 2019, IEEE Transactions on Image Processing.

[14]  Xiaorun Li,et al.  Endmember Bundle Extraction Based on Pure Pixel Index and Superpixel Segmentation , 2019, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium.

[15]  Zhenwei Shi,et al.  Multi-objective based spectral unmixing for hyperspectral images , 2017 .

[16]  Xuelong Li,et al.  A Classification-Based Model for Multi-Objective Hyperspectral Sparse Unmixing , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[17]  D. Roberts,et al.  Multiple Endmember Spectral Mixture Analysis (MESMA) to map burn severity levels from Landsat images in Mediterranean countries , 2013 .

[18]  Guangjian Yan,et al.  Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[19]  Changshan Wu,et al.  A Geographic Information-Assisted Temporal Mixture Analysis for Addressing the Issue of Endmember Class and Endmember Spectra Variability , 2017, Sensors.

[20]  C. Woodcock,et al.  Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? , 2001 .

[21]  Nathan S. Netanyahu,et al.  A Stepwise Analytical Projected Gradient Descent Search for Hyperspectral Unmixing and Its Code Vectorization , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Antonio J. Plaza,et al.  A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[23]  D. Roberts,et al.  Comparing endmember selection techniques for accurate mapping of plant species and land cover using imaging spectrometer data , 2012 .

[24]  Chengquan Huang,et al.  Monitoring fractional green vegetation cover dynamics over a seasonally inundated alpine wetland using dense time series HJ-1A/B constellation images and an adaptive endmember selection LSMM model , 2017 .

[25]  Maria C. Torres-Madronero,et al.  Integrating Spatial Information in Unsupervised Unmixing of Hyperspectral Imagery Using Multiscale Representation , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[26]  Bo Du,et al.  An image-based endmember bundle extraction algorithm using reconstruction error for hyperspectral imagery , 2016, Neurocomputing.

[27]  Jiang Li,et al.  Correction to "Wavelet-Based Feature Extraction for Improved Endmember Abundance Estimation in Linear Unmixing of Hyperspectral Signals" , 2004 .

[28]  Ricardo Augusto Borsoi,et al.  Improved Hyperspectral Unmixing with Endmember Variability Parametrized Using an Interpolated Scaling Tensor , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[29]  Xiao Xiang Zhu,et al.  SULoRA: Subspace Unmixing With Low-Rank Attribute Embedding for Hyperspectral Data Analysis , 2018, IEEE Journal of Selected Topics in Signal Processing.

[30]  Jun Li,et al.  Regional Clustering-Based Spatial Preprocessing for Hyperspectral Unmixing , 2018 .

[31]  Brian Curtiss,et al.  A method for manual endmember selection and spectral unmixing , 1996 .

[32]  D. Roberts,et al.  Wildfire temperature and land cover modeling using hyperspectral data , 2006 .

[33]  Qian Du,et al.  Modified Fisher's Linear Discriminant Analysis for Hyperspectral Imagery , 2007, IEEE Geoscience and Remote Sensing Letters.

[34]  Rob Heylen,et al.  A Geometric Unmixing Concept for the Selection of Optimal Binary Endmember Combinations , 2015, IEEE Geoscience and Remote Sensing Letters.

[35]  W. Verstraeten,et al.  Nonlinear Hyperspectral Mixture Analysis for tree cover estimates in orchards , 2009 .

[36]  Antonio J. Plaza,et al.  Collaborative Sparse Regression for Hyperspectral Unmixing , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[37]  Hannes Kazianka Objective Bayesian analysis for the normal compositional model , 2012, Comput. Stat. Data Anal..

[38]  Jean-Yves Tourneret,et al.  Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability , 2014, IEEE Transactions on Image Processing.

[39]  Paul D. Gader,et al.  A Gaussian Mixture Model Representation of Endmember Variability in Hyperspectral Unmixing , 2017, IEEE Transactions on Image Processing.

[40]  Chein-I Chang,et al.  A quantitative and comparative analysis of linear and nonlinear spectral mixture models using radial basis function neural networks , 2001, IEEE Trans. Geosci. Remote. Sens..

[41]  Antonio J. Plaza,et al.  Spectral Mixture Analysis of Hyperspectral Scenes Using Intelligently Selected Training Samples , 2010, IEEE Geoscience and Remote Sensing Letters.

[42]  Jiancheng Luo,et al.  Applying spectral mixture analysis for large-scale sub-pixel impervious cover estimation based on neighbourhood-specific endmember signature generation , 2015 .

[43]  Paul D. Gader,et al.  Sampling Piecewise Convex Unmixing and Endmember Extraction , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[44]  Carle M. Pieters,et al.  Non-Linear Spectral Un-Mixing Using Hapke Modeling: Application to Remotely Acquired M3 Spectra of Spinel Bearing Lithologies on the Moon , 2011 .

[45]  M. Cochrane Using vegetation reflectance variability for species level classification of hyperspectral data , 2000 .

[46]  W. Verstraeten,et al.  A Conceptual Framework for the Simultaneous Extraction of Sub-pixel Spatial Extent and Spectral Characteristics of Crops , 2009 .

[47]  Ying Wang,et al.  Spectral Unmixing Model Based on Least Squares Support Vector Machine With Unmixing Residue Constraints , 2013, IEEE Geoscience and Remote Sensing Letters.

[48]  Licheng Jiao,et al.  Hyperspectral Unmixing via Low-Rank Representation with Space Consistency Constraint and Spectral Library Pruning , 2018, Remote. Sens..

[49]  Claudia Giardino,et al.  The impact of the microphysical properties of aerosol on the atmospheric correction of hyperspectral data in coastal waters , 2015 .

[50]  Ben Somers,et al.  A weighted linear spectral mixture analysis approach to address endmember variability in agricultural production systems , 2009 .

[51]  Hermann Kaufmann,et al.  Comparison of Topographic Correction Methods , 2009, Remote. Sens..

[52]  Jean-Yves Tourneret,et al.  Estimating the Number of Endmembers in Hyperspectral Images Using the Normal Compositional Model and a Hierarchical Bayesian Algorithm , 2010, IEEE Journal of Selected Topics in Signal Processing.

[53]  Laurent Tits,et al.  Endmember Library Approaches to Resolve Spectral Mixing Problems in Remotely Sensed Data: Potential, Challenges, and Applications , 2016 .

[54]  Liming Zhang,et al.  Decomposition of mixed pixels based on bayesian self-organizing map and Gaussian mixture model , 2009, Pattern Recognit. Lett..

[55]  Erin B. Wetherley,et al.  Mapping spectrally similar urban materials at sub-pixel scales , 2017 .

[56]  Paul Honeine,et al.  Hyperspectral Unmixing in Presence of Endmember Variability, Nonlinearity, or Mismodeling Effects , 2015, IEEE Transactions on Image Processing.

[57]  Paul D. Gader,et al.  Piecewise Convex Multiple-Model Endmember Detection and Spectral Unmixing , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[58]  Daniel Schläpfer,et al.  CORRECTION OF SHADOWING IN IMAGING SPECTROSCOPY DATA BY QUANTIFICATION OF THE PROPORTION OF DIFFUSE ILLUMINATION , 2013 .

[59]  Gregory P. Asner,et al.  Scale dependence of biophysical structure in deforested areas bordering the Tapajós National Forest, Central Amazon , 2003 .

[60]  Hao Sun,et al.  Map-guided hyperspectral image superpixel segmentation using proportion maps , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[61]  Antonio J. Plaza,et al.  Dictionary pruning in sparse unmixing of hyperspectral data , 2012, 2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS).

[62]  Kai Zhao,et al.  On Spectral Unmixing Resolution Using Extended Support Vector Machines , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[63]  B. Gao,et al.  Radiative transfer codes applied to hyperspectral data for the retrieval of surface reflectance , 2002 .

[64]  Frédéric Baret,et al.  Fractional vegetation cover estimation algorithm for Chinese GF-1 wide field view data , 2016 .

[65]  Jocelyn Chanussot,et al.  Variability of the endmembers in spectral unmixing: Recent advances , 2016, 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

[66]  Yingbin Deng,et al.  Segmentation-based and rule-based spectral mixture analysis for estimating urban imperviousness , 2015 .

[67]  G. Asner Biophysical and Biochemical Sources of Variability in Canopy Reflectance , 1998 .

[68]  Xiaoqiang Lu,et al.  Projection-Based NMF for Hyperspectral Unmixing , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[69]  Reza Arablouei,et al.  Spectral Unmixing With Perturbed Endmembers , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[70]  Shi-Yong Yu,et al.  BEMMA: A Hierarchical Bayesian End-Member Modeling Analysis of Sediment Grain-Size Distributions , 2016, Mathematical Geosciences.

[71]  Pol Coppin,et al.  Endmember variability in Spectral Mixture Analysis: A review , 2011 .

[72]  D. Roberts,et al.  A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper , 2004 .

[73]  Mehdi Mokhtarzade,et al.  Reducing the Effect of the Endmembers' Spectral Variability by Selecting the Optimal Spectral Bands , 2017, Remote. Sens..

[74]  Rishi Ramakrishnan,et al.  Shadow compensation for outdoor perception , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[75]  Conghe Song,et al.  Spectral mixture analysis for subpixel vegetation fractions in the urban environment: How to incorporate endmember variability? , 2005 .

[76]  Manuel Graña,et al.  Endmember Extraction Methods: A Short Review , 2008, KES.

[77]  Qian Du,et al.  Modified multiple endmember spectral mixture analysis for mapping impervious surfaces in urban environments , 2014 .

[78]  Stéphane Jacquemoud,et al.  PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle , 2017 .

[79]  Derek M. Rogge,et al.  Iterative Spectral Unmixing for Optimizing Per-Pixel Endmember Sets , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[80]  Jocelyn Chanussot,et al.  Improved Local Spectral Unmixing of hyperspectral data using an algorithmic regularization path for collaborative sparse regression , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[81]  Johannes R. Sveinsson,et al.  Sparse Distributed Multitemporal Hyperspectral Unmixing , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[82]  S. Warren,et al.  A Model for the Spectral Albedo of Snow. I: Pure Snow , 1980 .

[83]  Jocelyn Chanussot,et al.  Dynamical Spectral Unmixing of Multitemporal Hyperspectral Images , 2015, IEEE Transactions on Image Processing.

[84]  John F. Mustard,et al.  Nonlinear spectral mixture modeling of lunar multispectral data: Implications for lateral transport , 1998 .

[85]  Ribana Roscher,et al.  Subpixel Mapping of Urban Areas Using EnMAP Data and Multioutput Support Vector Regression , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[86]  Xiuping Jia,et al.  Integration of Soft and Hard Classifications Using Extended Support Vector Machines , 2009, IEEE Geoscience and Remote Sensing Letters.

[87]  D. Lobell,et al.  Moisture effects on soil reflectance , 2002 .

[88]  Paul D. Gader,et al.  A Review of Nonlinear Hyperspectral Unmixing Methods , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[89]  Neil Sims,et al.  Spectral mixture analysis to monitor defoliation in mixed-aged Eucalyptus globulus Labill plantations in southern Australia using Landsat 5-TM and EO-1 Hyperion data , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[90]  Patrick Hostert,et al.  Ensemble Learning From Synthetically Mixed Training Data for Quantifying Urban Land Cover With Support Vector Regression , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[91]  A. Goetz,et al.  Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean , 2009 .

[92]  Gail P. Anderson,et al.  Atmospheric correction of spectral imagery: evaluation of the FLAASH algorithm with AVIRIS data , 2002, Applied Imagery Pattern Recognition Workshop, 2002. Proceedings..

[93]  Jean-Yves Tourneret,et al.  Hyperspectral Unmixing With Spectral Variability Using a Perturbed Linear Mixing Model , 2015, IEEE Transactions on Signal Processing.

[94]  Russell C Hardie,et al.  Stochastic spectral unmixing with enhanced endmember class separation. , 2004, Applied optics.

[95]  Benoit Rivard,et al.  Intra- and inter-class spectral variability of tropical tree species at La Selva, Costa Rica: Implications for species identification using HYDICE imagery , 2006 .

[96]  Glenn Healey,et al.  Models and methods for automated material identification in hyperspectral imagery acquired under unknown illumination and atmospheric conditions , 1999, IEEE Trans. Geosci. Remote. Sens..

[97]  Derek R. Peddle,et al.  Integration of a Geometric Optical Reflectance Model with an Evidential Reasoning Image Classifier for Improved Forest Information Extraction , 1999 .

[98]  Ruiliang Pu,et al.  Estimation of yellow starthistle abundance through CASI-2 hyperspectral imagery using linear spectral mixture models , 2006 .

[99]  Francesca Bovolo,et al.  A Novel Technique for Subpixel Image Classification Based on Support Vector Machine , 2010, IEEE Transactions on Image Processing.

[100]  Xiaojun Yang,et al.  Mapping vegetation in an urban area with stratified classification and multiple endmember spectral mixture analysis , 2013 .

[101]  Harumi Isaka,et al.  The effect of small topographic variations on reflectance , 2002, IEEE Trans. Geosci. Remote. Sens..

[102]  Wolfgang Krippner,et al.  Considering spectral variability for optical material abundance estimation , 2017 .

[103]  North F. Larsen,et al.  Use of shadows to retrieve water vapor in hazy atmospheres. , 2005, Applied optics.

[104]  Lianru Gao,et al.  Region-Based Estimate of Endmember Variances for Hyperspectral Image Unmixing , 2016, IEEE Geoscience and Remote Sensing Letters.

[105]  Alfred O. Hero,et al.  Nonlinear Unmixing of Hyperspectral Images: Models and Algorithms , 2013, IEEE Signal Processing Magazine.

[106]  Pau Closas,et al.  Kalman Filtering and Expectation Maximization for Multitemporal Spectral Unmixing , 2020, ArXiv.

[107]  Jean-Yves Tourneret,et al.  Bayesian Estimation of Linear Mixtures Using the Normal Compositional Model. Application to Hyperspectral Imagery , 2010, IEEE Transactions on Image Processing.

[108]  Chengbin Deng,et al.  Incorporating Endmember Variability into Linear Unmixing of Coarse Resolution Imagery: Mapping Large-Scale Impervious Surface Abundance Using a Hierarchically Object-Based Spectral Mixture Analysis , 2015, Remote. Sens..

[109]  Jean-Yves Tourneret,et al.  Online Unmixing of Multitemporal Hyperspectral Images Accounting for Spectral Variability , 2015, IEEE Transactions on Image Processing.

[110]  Antonio J. Plaza,et al.  MUSIC-CSR: Hyperspectral Unmixing via Multiple Signal Classification and Collaborative Sparse Regression , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[111]  M. Dayani,et al.  Geostatistical Assessment of the Spatial Distribution of Some Chemical Properties in Calcareous Soils , 2012 .

[112]  B. Kozintsev,et al.  Computations With Gaussian Random Fields , 1999 .

[113]  M. Griffin,et al.  Compensation of Hyperspectral Data for Atmospheric Effects , 2003 .

[114]  Maria Petrou,et al.  Confidence in linear spectral unmixing of single pixels , 1999, IEEE Trans. Geosci. Remote. Sens..

[115]  Ali Mohammad-Djafari,et al.  Bayesian analysis of spectral mixture data using Markov Chain Monte Carlo Methods , 2006 .

[116]  Zhiguo Jiang,et al.  Subspace Matching Pursuit for Sparse Unmixing of Hyperspectral Data , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[117]  Jocelyn Chanussot,et al.  Spectral Unmixing: A Derivation of the Extended Linear Mixing Model From the Hapke Model , 2019, IEEE Geoscience and Remote Sensing Letters.

[118]  Ryutaro Tateishi,et al.  Remote Sensing of Fractional Green Vegetation Cover Using Spatially-Interpolated Endmembers , 2012, Remote. Sens..

[119]  S. Jones,et al.  A linear physically-based model for remote sensing of soil moisture using short wave infrared bands , 2015 .

[120]  Chein-I Chang,et al.  Weighted abundance-constrained linear spectral mixture analysis , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[121]  Nicolas Dobigeon,et al.  Hyperspectral Unmixing With Spectral Variability Using Adaptive Bundles and Double Sparsity , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[122]  Jun Zhou,et al.  Hyperspectral Unmixing via $L_{1/2}$ Sparsity-Constrained Nonnegative Matrix Factorization , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[123]  K. McGwire,et al.  Hyperspectral mixture modeling for quantifying sparse vegetation cover in arid environments. , 2000 .

[124]  Michael K. Griffin,et al.  SENSITIVITY OF ATMOSPHERIC COMPENSATION MODEL RETRIEVALS TO INPUT PARAMETER SPECIFICATION , 1999 .

[125]  Satoru Yamamoto,et al.  Development of an application scheme for the SELENE/SP lunar reflectance model for radiometric calibration of hyperspectral and multispectral sensors , 2016 .

[126]  M. Ebadzadeh,et al.  Independent Base Vector Representation to Address Endmember Variability in Hyperspectral Unmixing , 2017, Journal of the Indian Society of Remote Sensing.

[127]  Jocelyn Chanussot,et al.  Hyperspectral Local Intrinsic Dimensionality , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[128]  Asad Mahmood,et al.  Estimation of the Intrinsic Dimension of Hyperspectral Images: Comparison of Current Methods , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[129]  Derek Anderson,et al.  Spectral Unmixing Cluster Validity Index for Multiple Sets of Endmembers , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[130]  Ye Zhang,et al.  SVM-Based Unmixing-to-Classification Conversion for Hyperspectral Abundance Quantification , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[131]  Fabio Maselli,et al.  Definition of Spatially Variable Spectral Endmembers by Locally Calibrated Multivariate Regression Analyses , 2001 .

[132]  Lloyd L. Coulter,et al.  Multidate MESMA for monitoring vegetation growth forms in southern California shrublands , 2018 .

[133]  Gabriele Arnold,et al.  A Model of Spectral Albedo of Particulate Surfaces: Implications for Optical Properties of the Moon , 1999 .

[134]  Jocelyn Chanussot,et al.  From local to global unmixing of hyperspectral images to reveal spectral variability , 2016, 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

[135]  José M. Bioucas-Dias,et al.  Semiblind Hyperspectral Unmixing in the Presence of Spectral Library Mismatches , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[136]  W. Verhoef Light scattering by leaf layers with application to canopy reflectance modelling: The SAIL model , 1984 .

[137]  Keshav Dev Singh,et al.  A comparative study of signal transformation techniques in automated spectral unmixing of infrared spectra for remote sensing applications , 2017 .

[138]  G. Asner,et al.  Spectral unmixing of vegetation, soil and dry carbon cover in arid regions: Comparing multispectral and hyperspectral observations , 2002 .

[139]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[140]  Jordan Ninin,et al.  Spectral Unmixing with Sparsity and Structuring Constraints , 2018, 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

[141]  Antonio J. Plaza,et al.  On the use of small training sets for neural network-based characterization of mixed pixels in remotely sensed hyperspectral images , 2009, Pattern Recognit..

[142]  Jun Huang,et al.  Hyperspectral Unmixing with Gaussian Mixture Model and Low-Rank Representation , 2019, Remote. Sens..

[143]  Antonio J. Plaza,et al.  Binary partition tree-based local spectral unmixing , 2014, 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

[144]  D. Roberts,et al.  Hierarchical Multiple Endmember Spectral Mixture Analysis (MESMA) of hyperspectral imagery for urban environments , 2009 .

[145]  Roger,et al.  Spectroscopy of Rocks and Minerals , and Principles of Spectroscopy , 2002 .

[146]  F. Baret,et al.  PROSPECT: A model of leaf optical properties spectra , 1990 .

[147]  Guolan Lu,et al.  Medical hyperspectral imaging: a review , 2014, Journal of biomedical optics.

[148]  Joshua N. Ash,et al.  Sum-Product Unmixing for Hyperspectral Analysis With Endmember Variability , 2018, IEEE Geoscience and Remote Sensing Letters.

[149]  Qian Du,et al.  PSO-EM: A Hyperspectral Unmixing Algorithm Based On Normal Compositional Model , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[150]  Paul D. Gader,et al.  Bootstrapping for Piece-Wise Convex Endmember Distribution Detection , 2012, 2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS).

[151]  Ying Wu,et al.  Regularized Simultaneous Forward–Backward Greedy Algorithm for Sparse Unmixing of Hyperspectral Data , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[152]  E. Milton,et al.  Spatial variability of the atmosphere over southern England, and its effect on scene-based atmospheric corrections , 2014 .

[153]  Xiaotong Zhang,et al.  A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data , 2017, Remote. Sens..

[154]  Alfred Stein,et al.  Abundance Estimation of Spectrally Similar Minerals by Using Derivative Spectra in Simulated Annealing , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[155]  Guillermo Sapiro,et al.  Learning Discriminative Sparse Representations for Modeling, Source Separation, and Mapping of Hyperspectral Imagery , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[156]  James A. Gardner,et al.  MODTRAN5: a reformulated atmospheric band model with auxiliary species and practical multiple scattering options , 2004, SPIE Asia-Pacific Remote Sensing.

[157]  Antonio J. Plaza,et al.  Automated Extraction of Image-Based Endmember Bundles for Improved Spectral Unmixing , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[158]  P. Curran,et al.  LIBERTY—Modeling the Effects of Leaf Biochemical Concentration on Reflectance Spectra , 1998 .

[159]  Ben Somers,et al.  Mapping impervious surface fractions using automated Fisher transformed unmixing , 2019, Remote Sensing of Environment.

[160]  D. Roberts,et al.  Mapping two Eucalyptus subgenera using multiple endmember spectral mixture analysis and continuum-removed imaging spectrometry data , 2011 .

[161]  Chengbin Deng,et al.  Automated Construction of Multiple Regional Libraries for Neighborhoodwise Local Multiple Endmember Unmixing , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[162]  Andreas Hueni,et al.  Cast Shadow Detection to Quantify the Aerosol Optical Thickness for Atmospheric Correction of High Spatial Resolution Optical Imagery , 2018, Remote. Sens..

[163]  D. Stein,et al.  Application of the normal compositional model to the analysis of hyperspectral imagery , 2003, IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003.

[164]  D. Peddle Spectral Mixture Analysis and Geometric-Optical Reflectance Modeling of Boreal Forest Biophysical Structure , 1999 .

[165]  E. R. Stoner,et al.  REFLECTANCE PROPERTIES OF SOILS , 1986 .

[166]  Lianru Gao,et al.  Bilinear normal mixing model for spectral unmixing , 2019, IET Image Process..

[167]  Alfred O. Hero,et al.  Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery , 2009, IEEE Transactions on Signal Processing.

[168]  Z. Chunhua,et al.  Spatial Variability of Soil Properties in a Long-Term Tobacco Plantation in Central China , 2010 .

[169]  David W. Messinger,et al.  Spatially Adaptive Hyperspectral Unmixing , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[170]  Laurent Tits,et al.  Hyperspectral shape-based unmixing to improve intra- and interclass variability for forest and agro-ecosystem monitoring , 2012 .

[171]  S. Linden,et al.  Support vector regression and synthetically mixed training data for quantifying urban land cover , 2013 .

[172]  Wenfei Luo,et al.  A PROSAIL-based spectral unmixing algorithm for solving vegetation spectral variability problem , 2018, International Symposium on Multispectral Image Processing and Pattern Recognition.

[173]  Rupert Müller,et al.  A New Approach for Endmember Extraction and Clustering Addressing Inter- and Intra-Class Variability via Multiscaled-Band Partitioning , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[174]  Aleixandre Verger,et al.  Optimal modalities for radiative transfer-neural network estimation of canopy biophysical characteristics: Evaluation over an agricultural area with CHRIS/PROBA observations , 2011 .

[175]  Maria C. Torres-Madronero,et al.  Unmixing Analysis of a Time Series of Hyperion Images Over the Guánica Dry Forest in Puerto Rico , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[176]  Meng Liu,et al.  An Orthogonal Fisher Transformation-Based Unmixing Method Toward Estimating Fractional Vegetation Cover in Semiarid Areas , 2017, IEEE Geoscience and Remote Sensing Letters.

[177]  Antonio J. Plaza,et al.  Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[178]  J. Boardman Automating spectral unmixing of AVIRIS data using convex geometry concepts , 1993 .

[179]  Jiancheng Shi,et al.  An Improved Endmember Selection Method Based on Vector Length for MODIS Reflectance Channels , 2015, Remote. Sens..

[180]  José M. Bioucas-Dias,et al.  Vertex component analysis: a fast algorithm to unmix hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[181]  Mohammad Mehdi Ebadzadeh,et al.  Endmember orthonormal mapping in hyperspectral mixture analysis to address endmember variability , 2016, Earth Science Informatics.

[182]  Richard J. Murphy,et al.  A Novel Endmember Bundle Extraction and Clustering Approach for Capturing Spectral Variability Within Endmember Classes , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[183]  T. Kemper,et al.  A new tool for variable multiple endmember spectral mixture analysis (VMESMA) , 2005 .

[184]  Jocelyn Chanussot,et al.  Variability of the endmembers in spectral unmixing , 2020 .

[185]  John P. Kerekes,et al.  Algorithm taxonomy for hyperspectral unmixing , 2000, SPIE Defense + Commercial Sensing.

[186]  José M. Bioucas-Dias,et al.  Does independent component analysis play a role in unmixing hyperspectral data? , 2003, IEEE Transactions on Geoscience and Remote Sensing.

[187]  Jocelyn Chanussot,et al.  Blind hyperspectral unmixing using an extended linear mixing model to address spectral variability , 2015, WHISPERS.

[188]  Ricardo Augusto Borsoi,et al.  Super-Resolution for Hyperspectral and Multispectral Image Fusion Accounting for Seasonal Spectral Variability , 2018, IEEE Transactions on Image Processing.

[189]  Changshan Wu,et al.  Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery , 2004 .

[190]  Susan L. Ustin,et al.  Evaluation of the potential of Hyperion for fire danger assessment by comparison to the Airborne Visible/Infrared Imaging Spectrometer , 2003, IEEE Trans. Geosci. Remote. Sens..

[191]  Rob Heylen,et al.  Hyperspectral Unmixing With Endmember Variability via Alternating Angle Minimization , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[192]  James A. Gardner,et al.  Algorithm for de-shadowing spectral imagery , 2002, SPIE Optics + Photonics.

[193]  Xiuping Jia,et al.  Spectral Unmixing in Multiple-Kernel Hilbert Space for Hyperspectral Imagery , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[194]  Alina Zare,et al.  Hyperspectral unmixing with endmember variability using Partial Membership Latent Dirichlet Allocation , 2016, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[195]  Susan L. Ustin,et al.  Evaluating Endmember and Band Selection Techniques for Multiple Endmember Spectral Mixture Analysis using Post-Fire Imaging Spectroscopy , 2018, Remote. Sens..

[196]  Gary A. Shaw,et al.  Spectral Imaging for Remote Sensing , 2003 .

[197]  Laurent Tits,et al.  A Comparison of Nonlinear Mixing Models for Vegetated Areas Using Simulated and Real Hyperspectral Data , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[198]  John B. Adams,et al.  Detectability of minerals on desert alluvial fans using reflectance spectra , 1987 .

[199]  Elisabetta Binaghi,et al.  Comparison of the multilayer perceptron with neuro-fuzzy techniques in the estimation of cover class mixture in remotely sensed data , 2001, IEEE Trans. Geosci. Remote. Sens..

[200]  Patrick Hostert,et al.  A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover , 2014, Remote. Sens..

[201]  Jocelyn Chanussot,et al.  Hyperspectral Image Unmixing With Endmember Bundles and Group Sparsity Inducing Mixed Norms , 2018, IEEE Transactions on Image Processing.

[202]  Qian Du,et al.  Equivalent-Sparse Unmixing Through Spatial and Spectral Constrained Endmember Selection From an Image-Derived Spectral Library , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[203]  Richard J. Murphy,et al.  Incorporating Spatial Information and Endmember Variability Into Unmixing Analyses to Improve Abundance Estimates , 2016, IEEE Transactions on Image Processing.

[204]  J. Chanussot,et al.  Hyperspectral Remote Sensing Data Analysis and Future Challenges , 2013, IEEE Geoscience and Remote Sensing Magazine.

[205]  Paul D. Gader,et al.  Nonlinear Spectral Unmixing With a Linear Mixture of Intimate Mixtures Model , 2014, IEEE Geoscience and Remote Sensing Letters.

[206]  Richard J. Murphy,et al.  A Novel Spectral Unmixing Method Incorporating Spectral Variability Within Endmember Classes , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[207]  Antonio J. Plaza,et al.  Sparse Unmixing of Hyperspectral Data , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[208]  B. Holben,et al.  Calibration of the AVHRR visible and near-IR bands by atmospheric scattering, ocean glint and desert reflection , 1993 .

[209]  Jing Jin,et al.  A Novel Approach Based on Fisher Discriminant Null Space for Decomposition of Mixed Pixels in Hyperspectral Imagery , 2010, IEEE Geoscience and Remote Sensing Letters.

[210]  Gregory Asner,et al.  Invasive Species Mapping in Hawaiian Rainforests Using Multi-Temporal Hyperion Spaceborne Imaging Spectroscopy , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[211]  Sildomar T. Monteiro,et al.  Evaluating Classification Techniques for Mapping Vertical Geology Using Field-Based Hyperspectral Sensors , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[212]  Ben Somers,et al.  Enhancing the performance of Multiple Endmember Spectral Mixture Analysis (MESMA) for urban land cover mapping using airborne lidar data and band selection , 2019, Remote Sensing of Environment.

[213]  Bo Du,et al.  An Image-Based Endmember Bundle Extraction Algorithm Using Both Spatial and Spectral Information , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[214]  Fenglei Fan,et al.  Enhancing endmember selection in multiple endmember spectral mixture analysis (MESMA) for urban impervious surface area mapping using spectral angle and spectral distance parameters , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[215]  Jan Verbesselt,et al.  Magnitude- and Shape-Related Feature Integration in Hyperspectral Mixture Analysis to Monitor Weeds in Citrus Orchards , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[216]  Paul E. Johnson,et al.  Simple algorithms for remote determination of mineral abundances and particle sizes from reflectance spectra , 1992 .

[217]  J. C. Price How unique are spectral signatures , 1994 .

[218]  Zhenwei Shi,et al.  ℓ0-based sparse hyperspectral unmixing using spectral information and a multi-objectives formulation , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[219]  Xiuping Jia,et al.  Spatially Constrained Multiple Endmember Spectral Mixture Analysis for Quantifying Subpixel Urban Impervious Surfaces , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[220]  D. Lobell,et al.  A Biogeophysical Approach for Automated SWIR Unmixing of Soils and Vegetation , 2000 .

[221]  Chia-Hsiang Lin,et al.  An Outlier-Insensitive Unmixing Algorithm With Spatially Varying Hyperspectral Signatures , 2019, IEEE Access.

[222]  Benoit Rivard,et al.  Mapping tropical dry forest succession using multiple criteria spectral mixture analysis , 2015 .

[223]  F. S. Grebbell Shadows. , 1987, The Ulster medical journal.

[224]  José M. Bioucas-Dias,et al.  Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing , 2010, 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

[225]  Changshan Wu,et al.  A geostatistical temporal mixture analysis approach to address endmember variability for estimating regional impervious surface distributions , 2016 .

[226]  Naoto Yokoya,et al.  An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing , 2018, IEEE Transactions on Image Processing.

[227]  Miina Rautiainen,et al.  Optical properties of leaves and needles for boreal tree species in Europe , 2013 .

[228]  K. C. Ho,et al.  Endmember Variability in Hyperspectral Analysis: Addressing Spectral Variability During Spectral Unmixing , 2014, IEEE Signal Processing Magazine.

[229]  Changshan Wu,et al.  Development of a Class-Based Multiple Endmember Spectral Mixture Analysis (C-MESMA) Approach for Analyzing Urban Environments , 2016, Remote. Sens..

[230]  Caiyun Zhang,et al.  Mapping urban land cover types using object-based multiple endmember spectral mixture analysis , 2014 .

[231]  Ricardo Augusto Borsoi,et al.  Low-Rank Tensor Modeling for Hyperspectral Unmixing Accounting for Spectral Variability , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[232]  Antonio J. Plaza,et al.  A Dynamic Unmixing Framework for Plant Production System Monitoring , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[233]  Mark Berman,et al.  A Comparison Between Three Sparse Unmixing Algorithms Using a Large Library of Shortwave Infrared Mineral Spectra , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[234]  Jean-Yves Tourneret,et al.  A Hierarchical Bayesian Model Accounting for Endmember Variability and Abrupt Spectral Changes to Unmix Multitemporal Hyperspectral Images , 2016, IEEE Transactions on Computational Imaging.

[235]  Jean-Pierre Bibring,et al.  Analysis of OMEGA/Mars Express data hyperspectral data using a Multiple-Endmember Linear Spectral Unmixing Model (MELSUM) : Methodology and first results , 2008 .

[236]  Paul D. Gader,et al.  PCE: Piecewise Convex Endmember Detection , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[237]  Youlu Bai,et al.  Spatial Variability of Soil Chemical Properties in the Reclaiming Marine Foreland to Yellow Sea of China , 2009 .

[238]  Daniel Clewley,et al.  Atmospheric Correction Performance of Hyperspectral Airborne Imagery over a Small Eutrophic Lake under Changing Cloud Cover , 2016, Remote. Sens..

[239]  Gregory Asner,et al.  Tree species mapping in tropical forests using multi-temporal imaging spectroscopy: Wavelength adaptive spectral mixture analysis , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[240]  Yannick Deville,et al.  Inertia-Constrained Pixel-by-Pixel Nonnegative Matrix Factorisation: a Hyperspectral Unmixing Method Dealing with Intra-class Variability , 2017, Remote. Sens..

[241]  Guillermo Botella,et al.  Parallel implementation of the multiple endmember spectral mixture analysis algorithm for hyperspectral unmixing , 2015, SPIE Remote Sensing.

[242]  Paul J. Curran,et al.  Spatial correlation in reflected radiation from the ground and its implications for sampling and mapping by ground-based radiometry , 1989 .

[243]  Laurent Tits,et al.  First results of quantifying nonlinear mixing effects in heterogeneous forests: A modeling approach , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[244]  Qiang Zhang,et al.  Detecting objects under shadows by fusion of hyperspectral and LiDAR DATA: A physical model approach , 2013, 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

[245]  Antonio J. Plaza,et al.  Hyperspectral Image Segmentation Using a New Spectral Unmixing-Based Binary Partition Tree Representation , 2014, IEEE Transactions on Image Processing.

[246]  Alfred Stein,et al.  Propagation of uncertainty in atmospheric parameters to hyperspectral unmixing , 2018 .

[247]  F. J. García-Haro,et al.  A Mixture Modeling Approach to Estimate Vegetation Parameters for Heterogeneous Canopies in Remote Sensing , 2000 .

[248]  Jocelyn Chanussot,et al.  Relationships Between Nonlinear and Space-Variant Linear Models in Hyperspectral Image Unmixing , 2017, IEEE Signal Processing Letters.

[249]  Hermann Kaufmann,et al.  Automated differentiation of urban surfaces based on airborne hyperspectral imagery , 2001, IEEE Trans. Geosci. Remote. Sens..

[250]  S. Tompkins,et al.  Optimization of endmembers for spectral mixture analysis , 1997 .

[251]  Ruiliang Pu,et al.  Conifer species recognition: An exploratory analysis of in situ hyperspectral data , 1997 .

[252]  Ben Somers,et al.  A Novel Spectral Library Pruning Technique for Spectral Unmixing of Urban Land Cover , 2017, Remote. Sens..

[253]  Paul Gader,et al.  Developing Spectral Libraries Using Multiple Target Multiple Instance Adaptive Cosine/Coherence Estimator , 2019, 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS).

[254]  Alfonso Fernández-Manso,et al.  Spectral unmixing , 2012 .

[255]  Antonio J. Plaza,et al.  Hyperspectral unmixingwith sparse group lasso , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[256]  José M. Bioucas-Dias,et al.  Fast Hyperspectral Unmixing in Presence of Nonlinearity or Mismodeling Effects , 2016, IEEE Transactions on Computational Imaging.

[257]  Ricardo Augusto Borsoi,et al.  Generalized Linear Mixing Model Accounting for Endmember Variability , 2017, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[258]  Antonio J. Plaza,et al.  A new extended linear mixing model to address spectral variability , 2014, 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

[259]  Ben Somers,et al.  Multi-temporal hyperspectral mixture analysis and feature selection for invasive species mapping in rainforests , 2013 .

[260]  Yosio Edemir Shimabukuro,et al.  Analyzing the spectral variability of tropical tree species using hyperspectral feature selection and leaf optical modeling , 2013 .

[261]  B. Hapke Bidirectional reflectance spectroscopy: 1. Theory , 1981 .

[262]  Maria Petrou,et al.  Mixture models with higher order moments , 1997, IEEE Trans. Geosci. Remote. Sens..

[263]  John F. Mustard,et al.  Photometric phase functions of common geologic minerals and applications to quantitative analysis of mineral mixture reflectance spectra , 1989 .

[264]  James K. Crowley,et al.  Visible and near‐infrared spectra of carbonate rocks: Reflectance variations related to petrographic texture and impurities , 1986 .

[265]  Ke Wang,et al.  Spectral Unmixing Using a Sparse Multiple-Endmember Spectral Mixture Model , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[266]  Laurent Tits,et al.  The Potential and Limitations of a Clustering Approach for the Improved Efficiency of Multiple Endmember Spectral Mixture Analysis in Plant Production System Monitoring , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[267]  Ben Somers,et al.  A multi-measurement vector approach for endmember extraction in urban environments , 2014 .

[268]  Dimitris G. Manolakis,et al.  Detection algorithms for hyperspectral imaging applications , 2002, IEEE Signal Process. Mag..

[269]  Matti Mottus,et al.  Seasonal Course of the Spectral Properties of Alder and Birch Leaves , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[270]  Antonio J. Plaza,et al.  Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[271]  Jihao Yin,et al.  Automatic endmember bundle unmixing methodology for lunar regional area mineral mapping , 2019, Icarus.

[272]  Ricardo Augusto Borsoi,et al.  A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability , 2018, IEEE Transactions on Image Processing.

[273]  S. Ustin,et al.  LEAF OPTICAL PROPERTIES: A STATE OF THE ART , 2000 .

[274]  Glenn J. Fitzgerald,et al.  Multiple shadow fractions in spectral mixture analysis of a cotton canopy , 2005 .

[275]  Chunhui Zhao,et al.  Hyperspectral Image Unmixing Based on Fast Kernel Archetypal Analysis , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[276]  Paul D. Gader,et al.  Spatial and Spectral Unmixing Using the Beta Compositional Model , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[277]  Benoit Rivard,et al.  Derivative spectral unmixing of hyperspectral data applied to mixtures of lichen and rock , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[278]  Cédric Richard,et al.  A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing , 2017, IEEE Geoscience and Remote Sensing Letters.

[279]  Kai Zhao,et al.  Reduction of Spectral Unmixing Uncertainty Using Minimum-Class-Variance Support Vector Machines , 2016, IEEE Geoscience and Remote Sensing Letters.

[280]  Antonio J. Plaza,et al.  Normal Endmember Spectral Unmixing Method for Hyperspectral Imagery , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[281]  C. Deng,et al.  A spatially adaptive spectral mixture analysis for mapping subpixel urban impervious surface distribution , 2013 .

[282]  Margaret E. Gardner,et al.  Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models , 1998 .

[283]  David R. Thompson,et al.  Leveraging in-scene spectra for vegetation species discrimination with MESMA-MDA , 2015 .

[284]  Ricardo Augusto Borsoi,et al.  Deep Generative Models for Library Augmentation in Multiple Endmember Spectral Mixture Analysis , 2019, ArXiv.

[285]  Kyu-Young Choi,et al.  An investigation into the properties of the dark endmember in spectral feature space , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[286]  S. Delalieux,et al.  An automated waveband selection technique for optimized hyperspectral mixture analysis , 2010 .

[287]  Paul D. Gader,et al.  A Spatial Compositional Model for Linear Unmixing and Endmember Uncertainty Estimation , 2015, IEEE Transactions on Image Processing.

[288]  Mark Andrews,et al.  Shadow modelling and correction techniques in hyperspectral imaging , 2013 .

[289]  Liquan Zhang,et al.  Multi-seasonal spectral characteristics analysis of coastal salt marsh vegetation in Shanghai, China , 2006 .

[290]  Laurent Tits,et al.  Quantifying Nonlinear Spectral Mixing in Vegetated Areas: Computer Simulation Model Validation and First Results , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[291]  J. Theiler,et al.  Spectral Variability of Remotely Sensed Target Materials: Causes, Models, and Strategies for Mitigation and Robust Exploitation , 2019, IEEE Geoscience and Remote Sensing Magazine.

[292]  A. Karnieli,et al.  Remote sensing of the seasonal variability of vegetation in a semi-arid environment , 2000 .