Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 5, Issue 2, 2012, Pages 354-379

  Bioucas Dias, José M ^a   Plaza, Antonio ^b   Dobigeon, Nicolas ^c   Parente, Mario ^d   Du, Qian ^e   Gader, Paul ^f,g   Chanussot, Jocelyn ^g  

^a INSTITUTO DE TELECOMUNICAÇÕES   (Portugal)

^b UNIVERSITY OF EXTREMADURA   (Spain)

^c UNIVERSITÉ DE TOULOUSE   (France)

^d University of Massachusetts   (United States)

^e MISSISSIPPI STATE UNIVERSITY   (United States)

^f University of Florida   (United States)

^g GRENOBLE INSTITUTE OF TECHNOLOGY   (France)

Author keywords

Hyperspectral imaging; hyperspectral remote sensing; image analysis; image processing; imaging spectroscopy; inverse problems; linear mixture; machine learning algorithms; nonlinear mixtures; pattern recognition; remote sensing; sparsity; spectroscopy; unmixing

Indexed keywords

HYPERSPECTRAL IMAGING; HYPERSPECTRAL REMOTE SENSING; IMAGING SPECTROSCOPY; LINEAR MIXTURES; NONLINEAR MIXTURES; SPARSITY; UNMIXING;

CAMERAS; ELECTROMAGNETIC WAVES; IMAGE ANALYSIS; IMAGE PROCESSING; INVERSE PROBLEMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; MIXTURES; PATTERN RECOGNITION; PIXELS; REMOTE SENSING; SPECTRAL RESOLUTION; SPECTROMETRY; SPECTROSCOPIC ANALYSIS; SPECTROSCOPY;

SPECTROMETERS;

ALGORITHM; IMAGE ANALYSIS; INVERSE PROBLEM; PATTERN RECOGNITION; REGRESSION ANALYSIS; REMOTE SENSING; SPECTROSCOPY;

EID: 84861772901     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2012.2194696     Document Type: Review

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