Robust Affine Set Fitting and Fast Simplex Volume Max-Min for Hyperspectral Endmember Extraction

Hyperspectral endmember extraction is to estimate endmember signatures (or material spectra) from the hyperspectral data of an area for analyzing the materials and their composition therein. The presence of noise and outliers in the data poses a serious problem in endmember extraction. In this paper, we handle the noise- and outlier-contaminated data by a two-step approach. We first propose a robust-affine-set-fitting algorithm for joint dimension reduction and outlier removal. The idea is to find a contamination-free data-representative affine set from the corrupted data, while keeping the effects of outliers minimum, in the least squares error sense. Then, we devise two computationally efficient algorithms for extracting endmembers from the outlier-removed data. The two algorithms are established from a simplex volume max-min formulation which is recently proposed to cope with noisy scenarios. A robust algorithm, called worst case alternating volume maximization (WAVMAX), has been previously developed for the simplex volume max-min formulation but is computationally expensive to use. The two new algorithms employ a different kind of decoupled max-min partial optimizations, wherein the design emphasis is on low-complexity implementations. Some computer simulations and real data experiments demonstrate the efficacy, the computational efficiency, and the applicability of the proposed algorithms, in comparison with the WAVMAX algorithm and some benchmark endmember extraction algorithms.

[1]  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..

[2]  Qian Du,et al.  Improving the quality of extracted endmembers , 2009, 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

[3]  Chong-Yung Chi,et al.  A Simplex Volume Maximization Framework for Hyperspectral Endmember Extraction , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Chein-I Chang,et al.  Anomaly detection and classification for hyperspectral imagery , 2002, IEEE Trans. Geosci. Remote. Sens..

[5]  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.

[6]  Antonio J. Plaza,et al.  Noise-robust spatial preprocessing prior to endmember extraction from hyperspectral data , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[7]  Marco Diani,et al.  A New Algorithm for Robust Estimation of the Signal Subspace in Hyperspectral Images in the Presence of Rare Signal Components , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Alan Edelman,et al.  The Geometry of Algorithms with Orthogonality Constraints , 1998, SIAM J. Matrix Anal. Appl..

[9]  Anthony M. Filippi,et al.  Support Vector Machine-Based Endmember Extraction , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Chong-Yung Chi,et al.  A Convex Analysis-Based Minimum-Volume Enclosing Simplex Algorithm for Hyperspectral Unmixing , 2009, IEEE Transactions on Signal Processing.

[11]  Maria Petrou,et al.  Robust Endmember Extraction in the Presence of Anomalies , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[12]  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.

[13]  Maria Petrou,et al.  Robust Endmember Extraction in the Presence of Anomalies , 2011, IEEE Trans. Geosci. Remote. Sens..

[14]  John F. Mustard,et al.  Spectral unmixing , 2002, IEEE Signal Process. Mag..

[15]  Chein-I Chang,et al.  Hyperspectral Data Exploitation , 2007 .

[16]  Joseph Meola,et al.  Modeling and estimation of signal-dependent noise in hyperspectral imagery. , 2011, Applied optics.

[17]  E. M. Winter,et al.  Anomaly detection from hyperspectral imagery , 2002, IEEE Signal Process. Mag..

[18]  B. Hapke Theory of reflectance and emittance spectroscopy , 1993 .

[19]  David Malah,et al.  Rank Estimation and Redundancy Reduction of High-Dimensional Noisy Signals With Preservation of Rare Vectors , 2007, IEEE Transactions on Signal Processing.

[20]  A. Barducci,et al.  Noise modelling and estimation of hyperspectral data from airborne imaging spectrometers , 2006 .

[21]  Chong-Yung Chi,et al.  A convex analysis-based minimum-volume enclosing simplex algorithm for hyperspectral unmixing , 2009, IEEE Trans. Signal Process..

[22]  Chein-I Chang,et al.  Sequential N-FINDR algorithms , 2008, Optical Engineering + Applications.

[23]  José M. Bioucas-Dias,et al.  A variable splitting augmented Lagrangian approach to linear spectral unmixing , 2009, 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

[24]  Chein-I Chang,et al.  Estimation of number of spectrally distinct signal sources in hyperspectral imagery , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Marco Diani,et al.  Signal-Dependent Noise Modeling and Model Parameter Estimation in Hyperspectral Images , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Chein-I. Chang Hyperspectral Data Exploitation: Theory and Applications , 2007 .

[27]  José M. P. Nascimento,et al.  Signal subspace identification in hyperspectral imagery , 2012 .

[28]  Fred A. Kruse,et al.  Comparison of airborne hyperspectral data and EO-1 Hyperion for mineral mapping , 2003, IEEE Trans. Geosci. Remote. Sens..

[29]  J. Neumann Zur Theorie der Gesellschaftsspiele , 1928 .

[30]  Russell C. Hardie,et al.  Application of the stochastic mixing model to hyperspectral resolution enhancement , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[31]  H. Kuhn The Hungarian method for the assignment problem , 1955 .

[32]  Chong-Yung Chi,et al.  A Convex Analysis Framework for Blind Separation of Non-Negative Sources , 2008, IEEE Transactions on Signal Processing.

[33]  Marco Diani,et al.  Hyperspectral Signal Subspace Identification in the Presence of Rare Signal Components , 2010, IEEE Transactions on Geoscience and Remote Sensing.

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

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

[36]  Chong-Yung Chi,et al.  Hyperspectral Data Geometry-Based Estimation of Number of Endmembers Using p-Norm-Based Pure Pixel Identification Algorithm , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[37]  Chong-Yung Chi,et al.  Chance-Constrained Robust Minimum-Volume Enclosing Simplex Algorithm for Hyperspectral Unmixing , 2011, IEEE Transactions on Geoscience and Remote Sensing.

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

[39]  R. Clark,et al.  The U. S. Geological Survey, Digital Spectral Library: Version 1 (0.2 to 3.0um) , 1993 .

[40]  Chein-I Chang,et al.  A New Growing Method for Simplex-Based Endmember Extraction Algorithm , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[41]  F. Kruse,et al.  Techniques Developed for Geologic Analysis of Hyperspectral Data Applied to Near-Shore Hyperspectral Ocean Data ** , 1997 .

[42]  Antonio Plaza,et al.  Hyperspectral unmixing: geometrical, statistical, and sparse regression-based approaches , 2010, Remote Sensing.

[43]  José M. Bioucas-Dias,et al.  Hyperspectral Subspace Identification , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[44]  Michael E. Winter A proof of the N-FINDR algorithm for the automated detection of endmembers in a hyperspectral image , 2004, SPIE Defense + Commercial Sensing.

[45]  Tian Han,et al.  Detection and correction of abnormal pixels in Hyperion images , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[46]  Kenneth W. Bauer,et al.  A Comparison of Multivariate Outlier Detection Methods For Finding Hyperspectral Anomalies , 2008 .