UnDAT: Double-Aware Transformer for Hyperspectral Unmixing

Deep-learning-based methods have attracted increasing attention on hyperspectral unmixing, where the transformer models have shown promising performance. However, recently proposed deep-learning-based hyperspectral unmixing methods usually tend to directly apply visual models, while ignoring the characteristics of hyperspectral imagery. In this article, we propose a novel double-aware transformer for hyperspectral Unmixing (UnDAT), which aims at simultaneously exploiting the region homogeneity and spectral correlation of hyperspectral imagery. One of the major assumptions of UnDAT is that hyperspectral remote-sensing images involve many homogeneous regions. Pixels inside a homogeneous region usually present similar spectral features, and the edge pixels are just the reverse. Another observation is that the pixel spectra are continuous and correlated. Based on the above assumption and observation, we construct the UnDAT by developing two modules: Score-based homogeneous-aware (SHA) module and the spectral group-aware (SGA) module. In the SHA module, a feature map rearrangement (FMR) approach is proposed to split the shallow feature maps from a linear encoder into an ordered homogeneous map (HomoMap) and an edge map and develop a homogenous region-aware strategy for deep feature representation. In the SGA module, the dependency among neighboring bands is described by dividing the hyperspectral image into multiple spectral groups and calculating the spectral similarity among bands within each group. Experiments on both real and synthetic datasets indicate the effectiveness of our model. We will publish the code of our approach if the article has the honor to be accepted.