Class-Level Joint Sparse Representation for Multifeature-Based Hyperspectral Image Classification

Recent studies show that different features can represent different characteristics of hyperspectral images, and a combination of them would have positive influence on classification. In this paper, we formulate the multifeature hyperspectral image classification as a joint sparse representation model which simultaneously represents the pixels of multiple features (spectral, shape, and texture) with a class-level sparse constraint. The proposed model enforces pixels in a small region of each type features to share the same sparsity pattern; at the same time, the pixels described by different features have freedom to adaptively choose their own appropriate atoms but still belong to the same class. Thus, the proposed model not only preserves the spatial information by joint sparse constraint but also utilizes additional complementary information from different features by class-level sparse constraint. Furthermore, we also kernelize the model to handle nonlinearity in the data. And a new version of simultaneous orthogonal matching pursuit is proposed to solve the aforementioned problems. Experiments on several real hyperspectral images indicate that the proposed algorithms provide a competitive performance when compared with several state-of-the-art algorithms.