Fast Multifeature Joint Sparse Representation for Hyperspectral Image Classification

Abstract

Since hyperspectral images (HSIs) usually have complex content and chaotic background, multiple kinds of features would be helpful for the classification task. Recently, representation-based methods with multifeature combination learning have been proposed. However, multifeature learning and the extended contextual information require much more computational burden, particularly for a large-scale dictionary case. In this letter, we propose a fast joint sparse representation classification method with multifeature combination learning for hyperspectral imagery. Once getting several complementary features (spectral, shape, and texture), the proposed model simultaneously acquires a representation vector for each kind of feature and imposes the joint sparsity $\boldsymbol{\ell}_{\mathrm{row},0} $ -norm regularization on the representation coefficients. The regularization can enforce the coefficients to share a common sparsity pattern, which preserves the cross-feature information. A new version of the simultaneous orthogonal matching pursuit is presented to solve the aforementioned problem because of its optimization with strong convergence guarantee and efficiency. Moreover, to further improve the classification performance, we incorporate contextual neighborhood information of the image into each kind of feature. Compared with state-of-the-art algorithms, it has been proved that the proposed algorithm with much less memory requirements performs tens to hundreds of times faster than those on real HSIs, while providing the same (or even better) accuracy.