Fast forward feature selection of hyperspectral images for classification with Gaussian mixture models

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

Volumn 8, Issue 6, 2015, Pages 2824-2831

  Fauvel, Mathieu ^a,b   Dechesne, Clément ^a,b   Zullo, Anthony ^a,b   Ferraty, Frédéric ^a  

^a UNIVERSITÉ DE TOULOUSE   (France)

^b UMR 1201 Dynafor   (France)

Author keywords

Gaussian mixture model (GMM); hyperspectral image classification; nonlinear feature selection; parsimony

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); COMMUNICATION CHANNELS (INFORMATION THEORY); FEATURE EXTRACTION; GAUSSIAN DISTRIBUTION; INDEPENDENT COMPONENT ANALYSIS; ITERATIVE METHODS; OBJECT RECOGNITION; SPECTROSCOPY;

CLASSIFICATION ACCURACY; EFFICIENT IMPLEMENTATION; GAUSSIAN MIXTURE MODEL; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL IMAGE CLASSIFICATION; K FOLD CROSS VALIDATIONS; NONLINEAR FEATURES; PARSIMONY;

IMAGE CLASSIFICATION;

ACCURACY ASSESSMENT; ALGORITHM; GAUSSIAN METHOD; IMAGE CLASSIFICATION; NUMERICAL MODEL; PARSIMONY ANALYSIS; SATELLITE DATA; SATELLITE IMAGERY;

EID: 85027943988     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2015.2441771     Document Type: Article

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