Local SVM approaches for fast and accurate classification of remote-sensing images

International Journal of Remote Sensing

Volumn 33, Issue 19, 2012, Pages 6186-6201

  Segata, Nicola ^a,b   Pasolli, Edoardo ^a   Melgani, Farid ^a   Blanzieri, Enrico ^a  

^a UNIVERSITY OF TRENTO   (Italy)

^b HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

REMOTE SENSING; SUPPORT VECTOR MACHINES;

CLASSIFICATION ACCURACY; CLASSIFICATION OF REMOTE SENSING IMAGE; COMPUTATIONAL COSTS; COMPUTATIONAL LIMITATIONS; EXPERIMENTAL ANALYSIS; LOCAL INFORMATION; REMOTE SENSING DATA; REMOTE SENSING IMAGE CLASSIFICATION;

IMAGE CLASSIFICATION;

ARTIFICIAL INTELLIGENCE; IMAGE CLASSIFICATION; NUMERICAL MODEL; REMOTE SENSING;

EID: 84860909030     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2012.678947     Document Type: Article

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