Multifeature-Based Discriminative Label Consistent K-SVD for Hyperspectral Image Classification

Abstract

Given the chaotic background and complex content of hyperspectral images (HSIs), the complementary information of multiple features is beneficial for HSI classification. Many multifeature-based sparse representation methods have been recently proposed. However, the large-scale dictionary problem and the optimal multifeature combination strategy have remained unsolved. For the aforementioned problem, we propose a multifeature-based discriminative label consistent K-singular value decomposition (MF-LC-KSVD) algorithm for HSI classification. In the training step of the approach, a multifeature dictionary and a combined linear classifier are obtained by jointly utilizing the LC-KSVD algorithm on the extracted features. The multifeature classifier can automatically determine the relative importance of different features for classification. The “discriminative sparse-code error” constraint in the LC-KSVD facilitates the sharing of the same spare pattern of the atoms in a special feature from the same class, thereby increasing the discrimination of the linear classifier. In the testing step, a multifeature sparse matrix is obtained by introducing the above overcomplete multifeature dictionary into multifeature-based adaptive sparse representation. The class label is then predicted by applying the classifier on multifeature sparse codes. In addition, we introduce the shape-adaptive spatial region to incorporate the information of neighbor pixels, hence further improving the classification performance. Compared with other multifeature-based sparse representation methods, our proposed MF-LC-KSVD is proven to have better classification accuracy.