Subpixel Spectral Variability Network for Hyperspectral Image Classification

Deep learning-based frameworks have shown great potential in the field of hyperspectral image (HSI) classification owing to their superior modeling capabilities. However, the existence of mixed pixels and spectral heterogeneity limits the discriminant performance of the classifier, which makes it impossible to distinguish the mixed spectra effectively in actual scenarios. To address this gap, we propose a subpixel spectral variability network ( $\text {S}^{2}\text {VNet}$ ) for HSI classification, which incorporates complete subpixel information and class features modeled by spectral variability and nonlinear mixture characteristics to enhance classification performance. $\text {S}^{2}\text {VNet}$ is capable of extracting endmembers and abundances based on the nonlinear autoencoder (AE) framework and estimating variability parameters by simultaneously considering scaling factors and perturbation terms to ensure accurate endmember construction. The enhanced subpixel fusion module is further designed to automatically integrate three aspects of abundances, spectral cosine correlation information, and pixel-level class features to provide a robust joint representation for the classifier. Extensive experiments on four public HSI datasets demonstrate the superiority and generalization of the proposed method when benchmarked with state-of-the-art methods. The code will be available at https://github.com/hanzhu97702/S2VNet.