Hyperspectral Imagery Classification Aiming at Protecting Classes of Interest

Classification is an important technique of hyperspetral imagery processing. In traditional classification methods of hyperspectral imagery, all classes are treated equally. Some of them, however, should be given more regard, and so, it is significant to emphasize particularly on the analysis effect of classes of interest. In this case, two kinds of processing methods are proposed to protect classes of interest in process of least square SVM based classification: deleting training samples and changing diagonal elements. In former method, by deleting samples of uninterested classes in process of SVM training, interested classes are left and their classification accuracies are improved greatly. In latter method, by attaching different weights to diagonal elements of punishment matrix, samples of interested classes are given more regard and so the corresponding classification accuracies are improved. Elaborate experiments show that the proposed methods can improve the classification effect of classes of interest.

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