Unrolling Plug-and-Play Network for Hyperspectral Unmixing

Min Zhao, Linruize Tang, Jie Chen

Research output: Contribution to journalArticlepeer-review

Abstract

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 image, however, they tend to be less physical interpretable. Conventional unmixing methods are with 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. Secondly, 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.

Original languageEnglish
JournalIEEE Transactions on Geoscience and Remote Sensing
DOIs
StateAccepted/In press - 2025

Keywords

  • ADMM
  • Hyperspectral unmixing
  • inner priors
  • outer priors
  • plug-and-play
  • unrolling

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