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Automatic selection of informative samples for SVM-based classification of hyperspectral data using limited training sets
2010
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
In this work, we focus on how to select the most highly informative samples for effectively training support vector machine (SVM) classifiers in remotely sensed hyperspectral data classification. This issue is investigated by comparing different unsupervised algorithms which account for the spectral purity of training samples in the process of selecting those samples for classification purposes. Sample sets obtained using these algorithms are used to train an SVM architecture implemented using
doi:10.1109/whispers.2010.5594931
dblp:conf/whispers/PlazaP10
fatcat:mm2j6rnrnfes3pj6j4urokxeda