Unrolling Plug-and-Play Network for Hyperspectral Unmixing

Deep learning-based unmixing methods have received great attention in recent years and achieved remarkable performance. These methods employ a data-driven approach to extract structure features from hyperspectral images; however, they tend to be less physically interpretable. Conventional unmixing methods have much more interpretability, whereas they require manually designing regularization and choosing penalty parameters. To overcome these limitations, we propose a novel unmixing method by unrolling the plug-and-play unmixing algorithm to conduct the deep architecture. Our method integrates both inner and outer priors. The carefully designed unfolding deep architecture is used to learn the spectral and spatial information from the hyperspectral image, which we refer to as inner priors. Additionally, our approach incorporates deep denoisers that have been pretrained on a large volume of image data to leverage the outer priors. Second, we design a dynamic convolution to model the multiscale information. Different scales are fused with an attention module. Experimental results of both synthetic and real datasets demonstrate that our method outperforms compared methods.