期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2015
卷号：8
期号：10
页码：11-18
DOI：10.14257/ijsip.2015.8.10.02
出版社：SERSC
摘要：In this paper we have proposed a new marker selection technique using Support Vector Machine over-fitting. Markers are the most reliable pixels in a class. We used our proposed technique to do classification of hyperspectral image with very low training samples, as low as one pixel per class. We have used both spectral and spatial information to improve the classification results. The spatial information is extracted using Extended Morphological Profiles with duality. Nonparametric supervised feature extraction methods are used to eliminate the redundant and irrelevant information in both spatial and spectral domains. In the end we have done experimentation to verify our proposed approach. The experimentation results show that when non-parametric weighted feature extraction method is used we get better classification results. The classification maps shows that even with just one training sample per class we still can get a reliably reasonable classification map
关键词：Classification; feature extraction; hyperspectral images; support vector ; machine
