A Reversible Generative Network for Hyperspectral Unmixing With Spectral Variability

Spectral variability is one of the challenges for hyperspectral unmixing. Recently, deep generative models are developed to describe the spectral variability, which have attracted increasing attention. However, generative unmixing methods may suffer the problems of mode collapse and image blur, which tend to generate uncontrollable endmember distribution. To address this issue, in this article, we propose a reversible generative network (Rev-Net) for hyperspectral imagery unmixing, which targets at the spectral variability challenge. Our motivation is that if the endmember distribution can be described by an explicit mathematical expression and the expression is reversible, then the generation process will be more stable. To achieve this purpose, Rev-Net mainly includes two contributions: a flow-based endmember learning module and a theoretical proof for the reversibility of the endmember generation process. In the endmember learning module, we develop a new flow-based structure with a series of reversible transformation, so as to obtain an explicit mathematical expression for the endmember distribution. Moreover, to guarantee the existence of the explicit expression, we have theoretically proven the reversibility of the endmember learning module. Through the flow-based endmember learning module and the correspond theoretical analysis, the proposed Rev-Net can make the endmember generation process more stable and thus avoiding the problems of mode collapse and image blur. In addition, we also construct an abundance guidance module to further assist in the generation process of endmember by image reconstruction. Experimental results on real hyperspectral datasets and synthetic datasets indicate that Rev-Net has certain competitiveness. The codes are available at https://github.com/Lab-PANbin/Rev-Net.