Abstract
Representation-based classifiers such as nearest regularized subspace (NRS) have been recently developed for hyperspectral image classification. The joint collaborative representation (JCR) and the weighted JCR (WJCR) methods added spatial information to the pixel-wise NRS classifier. While JCR adopts the same weights for extraction of spatial features from the surrounding pixels, WJCR uses the similarity between the central pixel and its surroundings to assign different weights to neighbor pixels. Two improved versions of WJCR are introduced in this paper. The first method, WJCR based on median-mean line, is proposed to cope with the negative effect of outlying neighbors. The second method, WJCR based on angular separation (AS), uses the benefits of the AS measurement to decrease the contribution of redundant information due to the highly correlated neighbors. The experimental results on some real hyperspectral data sets show the good efficiency of the proposed methods compared to other state-of-the-art NRS-based classifiers.
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