Automatic selection of informative samples for SVM-based classification of hyperspectral data using limited training sets

Antonio Plaza, Javier Plaza
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
more » ... ifferent kernels, with the ultimate goal of exploring the suitability of the aforementioned algorithms to reduce the number of training samples required by these architectures in the context of hyperspectral image classification. Experimental results are provided using the full version of a hyperspectral data set collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Indian Pines region in Northwestern Indiana. Index Terms-Machine learning, hyperspectral image classification, support vector machines (SVMs), automatic selection of training samples 978-1-4244-8907-7/10/$26.00 ©2010 IEEE
doi:10.1109/whispers.2010.5594931 dblp:conf/whispers/PlazaP10 fatcat:mm2j6rnrnfes3pj6j4urokxeda