Superpixel-Level Hybrid Discriminant Analysis for Hyperspectral Image Feature Extraction

For hyperspectral image (HSI) classification, it is a challenging problem to learn highly discriminative features, since the complex local/nonlocal spatial-spectral association is difficult to be accurately characterized. Focused on this issue, a novel superpixel-level hybrid discriminant analysis (SHDA) method is proposed, in this article. Here, the SHDA method takes advantage of superpixel's merit in characterizing spatial-spectral shape-adaptive structure and the powerful capability of discriminant analysis in enhancing class-separability to learn the feature representation. Moreover, the local/nonlocal spatial-spectral correlation information among/between superpixels is effectively excavated and fused in one framework to further improve the classification performance of features. This is achieved by first designing two specific discriminant analysis modules, i.e., superpixel-level local discriminant analysis (SLDA) and superpixel-level nonlocal discriminant analysis (SNDA). In the SLDA, adaptively weighted scatter matrices are defined to characterize the local spectral similarity within each superpixel and the discrepancy among adjacent superpixels. In the SNDA, superpixel-level graphs are built for capturing the nonlocal contextual information, where the weights of graphs are estimated based on the most similar and dissimilar superpixels. Then, the SLDA and the SNDA are effectively fused to construct the total intra/intersuperpixel scatter matrices. Finally, a joint projection transformation is obtained by solving a simple generalized eigenvalue problem. In this way, the HSI data can be projected from a high-dimensional data space into a low-dimensional feature space, where different classes of land covers can be more accurately distinguished. Experimental results on three real hyperspectral datasets indicate that the proposed SHDA method outperforms several state-of-the-art techniques.