Classification in high-dimensional feature spaces-assessment using SVM, IVM and RVM with focus on simulated EnMAP data

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

Volumn 5, Issue 2, 2012, Pages 436-443

  Braun, Andreas Ch ^a   Weidner, Uwe ^a   Hinz, Stefan ^a  

^a KARLSRUHE INSTITUTE OF TECHNOLOGY   (Germany)

Author keywords

Classification; EnMAP; import vector machines; relevance vector machines; support vector machines

Indexed keywords

APPLICATION-ORIENTED; BAYES THEOREM; CLASSIFICATION ACCURACY; CLASSIFICATION RESULTS; ENMAP; ENVIRONMENTAL MAPPING; HIGH DIMENSIONAL DATASETS; HIGH-DIMENSIONAL; HUGHES PHENOMENON; HYPERSPECTRAL; MULTISPECTRAL DATASETS; RELEVANCE VECTOR MACHINE; REMOTE SENSING DATA; SIMULATED DATASETS; VECTOR MACHINES;

CLASSIFICATION (OF INFORMATION); PROBABILITY DISTRIBUTIONS; REMOTE SENSING; RESEARCH;

SUPPORT VECTOR MACHINES;

ACCURACY ASSESSMENT; COMPUTER SIMULATION; DATA SET; IMAGE CLASSIFICATION; MAPPING; METHODOLOGY; MULTISPECTRAL IMAGE; PROBABILITY; REMOTE SENSING;

EID: 84861757634     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2012.2190266     Document Type: Article

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