Hyperspectral image classification using support vector machines: A comparison with decision tree and neural network classifiers

American Society for Photogrammetry and Remote Sensing - Annual Conference 2005 - Geospatial Goes Global: From Your Neighborhood to the Whole Planet

Volumn 2, Issue , 2005, Pages 1130-1138

  Watanachaturaporn, Pakorn ^a   Arora, Manoj K ^b   Varshney, Pramod K ^a  

^a Syracuse University   (United States)

^b INDIAN INSTITUTE OF TECHNOLOGY ROORKEE   (India)

Author keywords

[No Author keywords available]

Indexed keywords

BEST VALUE; BP NEURAL NETWORK CLASSIFIER; CLASSIFICATION ACCURACY; COMPUTATIONALLY EFFICIENT; CONFIDENCE LEVELS; DECISION TREE CLASSIFIERS; HIGH-DIMENSIONAL DATASET; HYPERSPECTRAL DATA; HYPERSPECTRAL IMAGE CLASSIFICATION; IN-HOUSE CODES; KAPPA COEFFICIENT; MATLAB ENVIRONMENT; MULTISPECTRAL REMOTE SENSING; NEURAL NETWORK CLASSIFIER; NON-PARAMETRIC; PARAMETRIC CLASSIFIER; POLYNOMIAL KERNELS; RADIAL BASIS FUNCTION NEURAL NETWORKS; RBF CLASSIFIERS; RBF NEURAL NETWORK; SPECTRAL BAND; SVM CLASSIFICATION; SVM CLASSIFIERS; SVM-BASED CLASSIFIERS;

DECISION TREES; IMAGE CLASSIFICATION; NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; REMOTE SENSING; SPECTROSCOPY;

SUPPORT VECTOR MACHINES;

EID: 84869043828     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

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