A Sturdy Nonlinear Hyperspectral Unmixing

Hyperspectral unmixing (HSU) is a way to process the prediction of the existing endmembers and the fractional abundances (FA) available in all pixels in the hyperspectral images. However, in a prac...

[1]  Yannick Deville,et al.  Linear–Quadratic Mixing Model for Reflectances in Urban Environments , 2014, IEEE Transactions on Geoscience and Remote Sensing.

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

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

[4]  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.

[5]  Mario Winter,et al.  N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data , 1999, Optics & Photonics.

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

[7]  Jean-Yves Tourneret,et al.  Bilinear models for nonlinear unmixing of hyperspectral images , 2011, 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

[8]  Jean-Yves Tourneret,et al.  Supervised Nonlinear Spectral Unmixing Using a Postnonlinear Mixing Model for Hyperspectral Imagery , 2012, IEEE Transactions on Image Processing.

[9]  Colm P. O'Donnell,et al.  Hyperspectral imaging – an emerging process analytical tool for food quality and safety control , 2007 .

[10]  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.

[11]  Angshul Majumdar,et al.  Hyperspectral Unmixing in the Presence of Mixed Noise Using Joint-Sparsity and Total Variation , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

[13]  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.

[14]  Jean-Yves Tourneret,et al.  Nonlinear unmixing of hyperspectral images using a generalized bilinear model , 2011, 2011 IEEE Statistical Signal Processing Workshop (SSP).

[15]  John R. Miller,et al.  Comparative study between a new nonlinear model and common linear model for analysing laboratory simulated‐forest hyperspectral data , 2009 .

[16]  Hichem Snoussi,et al.  Bayesian Estimation of Smooth Altimetric Parameters: Application to Conventional and Delay/Doppler Altimetry , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Chris H. Q. Ding,et al.  Robust nonnegative matrix factorization using L21-norm , 2011, CIKM '11.

[18]  Steve McLaughlin,et al.  Robust Linear Spectral Unmixing Using Anomaly Detection , 2015, IEEE Transactions on Computational Imaging.

[19]  Sanjay Ghosh,et al.  Artifact reduction for separable nonlocal means , 2017, J. Electronic Imaging.

[20]  A. Ben Hamza,et al.  Reconstruction of reflectance spectra using robust nonnegative matrix factorization , 2006, IEEE Transactions on Signal Processing.

[21]  Jean-Yves Tourneret,et al.  Detecting nonlinear mixtures in hyperspectral images , 2012, 2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS).

[22]  Xiaofei He,et al.  Robust non-negative matrix factorization , 2011 .

[23]  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.

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

[25]  Nicolas Dobigeon,et al.  Spectral mixture analysis of EELS spectrum-images. , 2012, Ultramicroscopy.

[26]  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.

[27]  Sanjay Ghosh,et al.  Fast separable nonlocal means , 2016, J. Electronic Imaging.

[28]  Thierry Blu,et al.  The SURE-LET Approach to Image Denoising , 2007, IEEE Transactions on Image Processing.

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

[30]  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.

[31]  José M. Bioucas-Dias,et al.  Does independent component analysis play a role in unmixing hyperspectral data? , 2005, IEEE Trans. Geosci. Remote. Sens..

[32]  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.

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

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

[35]  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).

[36]  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.

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

[38]  Chein-I Chang,et al.  Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery , 2001, IEEE Trans. Geosci. Remote. Sens..

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

[40]  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.

[41]  C. Stein Estimation of the Mean of a Multivariate Normal Distribution , 1981 .

[42]  Guillermo Sapiro,et al.  Real-time Online Singing Voice Separation from Monaural Recordings Using Robust Low-rank Modeling , 2012, ISMIR.

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

[44]  Athanasios A. Rontogiannis,et al.  On the unmixing of MEx/OMEGA hyperspectral data , 2010, 1112.1527.

[45]  Johannes R. Sveinsson,et al.  Hyperspectral Unmixing With $l_{q}$ Regularization , 2014, IEEE Transactions on Geoscience and Remote Sensing.

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

[47]  Jie Chen,et al.  Nonlinear Unmixing of Hyperspectral Data Based on a Linear-Mixture/Nonlinear-Fluctuation Model , 2013, IEEE Transactions on Signal Processing.

[48]  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.

[49]  José M. Bioucas-Dias,et al.  Nonlinear mixture model for hyperspectral unmixing , 2009, Remote Sensing.

[50]  Joseph N. Wilson,et al.  Using physics-based macroscopic and microscopic mixture models for hyperspectral pixel unmixing , 2012, Defense + Commercial Sensing.