Semi-Supervised Based Hyperspectral Imagery Classification
نویسندگان
چکیده
Hyperspectral imagery classification is a challenging problem. Wherein, the high number of spectral channels and the high cost of true sample labeling greatly reduce the classification precision. In this paper, we proposed a semi-supervised method, which combine linear discriminant analysis and manifold learning, to improve the precision of hyperspectral imagery classification. Experimental results showed that new method had provided considerable insight on the band extraction problem and the new features were good for land-cover classification. Copyright © 2013 IFSA.
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