Spectral Variability Augmented Two-Stream Network for Hyperspectral Sparse Unmixing

Abstract

Deep learning-based methods have drawn great attention in hyperspectral unmixing and obtained promising performance due to their powerful learning capability. However, few existing networks explicitly deal with the spectral variability inevitably present in hyperspectral images, limiting their fitting performance. In this letter, a spectral variability augmented two-stream network (SVATN) is designed to explicitly address the problem of spectral variability in a deep convolutional network for sparse unmixing. Specifically, the proposed SVATN maps a random input to coefficients of spectral variability in addition to abundances of endmembers, in which spectral variability is accommodated by the linear mixture model as an augmented item. Moreover, a spatial-spectral correlation-based variability extraction method (SSCVE) is proposed to construct a spectral variability library, which serves as priors in the loss function to optimize the proposed SVATN. Experiments over synthetic and real data sets demonstrate the superiority of the proposed SVATN over several state-of-the-art methods. The code of our proposed method is released at: https://github.com/MeiShaohui/SVATN.