Self-Supervised Robust Deep Matrix Factorization for Hyperspectral Unmixing

Abstract

Hyperspectral unmixing is a critical step to process hyperspectral images (HSIs). Nonnegative matrix factorization (NMF) has drawn extensive attention in remotely sensed hyperspectral unmixing since it does not require prior knowledge about the pure spectral constituents (endmembers) in the scene. However, this approach is normally implemented as a single-layer procedure, which does not allow for a refinement of the obtained endmember abundances. In addition, HSIs suffer from the interference of sparse noise (besides Gaussian noise), which brings challenges when pursuing efficient hyperspectral unmixing. To address these issues, we propose a new self-supervised robust deep matrix factorization (SSRDMF) model for hyperspectral unmixing, which consists of two parts: encoder and decoder. In the encoder, a multilayer nonlinear structure is designed to directly map the observed HSI data to the corresponding abundances. The abundances are then decoded by the decoder, in which the connected weights are treated as the extracted endmembers. By modeling the sparse noise explicitly, the proposed method can reduce the effect caused by both Gaussian and sparse noise. Furthermore, a self-supervised constraint is included for exploring the spectral information, which is beneficial to further improve unmixing performance. To validate our method, we have conducted extensive experiments on both synthetic and real datasets. Our experiments reveal that our newly developed SSRDMF achieves superior unmixing performance compared to other state-of-the-art methods.