A quantitative and comparative assessment of unmixing-based feature extraction techniques for hyperspectral image classification

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

Volumn 5, Issue 2, 2012, Pages 421-435

  Dópido, Inmaculada ^a   Villa, Alberto ^b,c   Plaza, Antonio ^a   Gamba, Paolo ^d  

^a UNIVERSITY OF EXTREMADURA   (Spain)

^b GIPSA LAB   (France)

^c UNIVERSITY OF ICELAND   (Iceland)

^d UNIVERSITY OF PAVIA   (Italy)

Author keywords

Hyperspectral image classification; spatial spectral integration; spectral unmixing; support vector machines (SVMs); unmixing based feature extraction

Indexed keywords

COMPARATIVE ASSESSMENT; CURSE OF DIMENSIONALITY; FEATURE EXTRACTION TECHNIQUES; GENERALIZATION CAPABILITY; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL; HYPERSPECTRAL IMAGE CLASSIFICATION; MACHINE LEARNING TECHNIQUES; MINIMUM NOISE FRACTION; PROCESSING CHAIN; SPATIAL INFORMATIONS; SPATIAL RESOLUTION; SPECTRAL INFORMATION; SPECTRAL UNMIXING; STATISTICAL TRANSFORMATION; SUB PIXELS; SUPERVISED CLASSIFICATION; UNSUPERVISED CLUSTERING;

IMAGE CLASSIFICATION; PRINCIPAL COMPONENT ANALYSIS; SUPPORT VECTOR MACHINES;

FEATURE EXTRACTION;

ASSESSMENT METHOD; IMAGE CLASSIFICATION; PATTERN RECOGNITION; REMOTE SENSING; SPATIAL RESOLUTION;

EID: 84861725237     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2011.2176721     Document Type: Article

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