Edge-preserving-based collaborative representation for spectral-spatial classification

ABSTRACT

Recently, the nearest regularized subspace (NRS) classifier and its spectral–spatial versions such as joint collaborative representation (JCR) and weighted JCR (WJCR) have gained an increasing interest in the hyperspectral image classification. JCR and WJCR average each pixel with its neighbours in a spatial neighbourhood window. The use of spatial information as averaging of pixels in a local window may degrade the classification accuracy in the neighbourhood of discontinuities and class boundaries. We propose the edge-preserving-based collaborative representation (EPCR) classifier in this article, which overcomes this problem by using the edge image estimated by the original full-band hyperspectral image. The estimated edge image is used for calculation of the weights of neighbours and also the final residuals in the collaborative representation classifier. The advantage of multiscale spatial window is also assessed in this work. Moreover, the kernelized versions of NRS and its improved versions are developed in this article. Our experimental results on several popular hyperspectral images indicate that EPCR and its kernelized version are superior to some state-of-the-art classification methods.

Disclosure statement

No potential conflict of interest was reported by the authors.