Hyperspectral nonlinear unmixing by using plug-and-play prior for abundance maps

Zhicheng Wang, Lina ZHUANG, Lianru Gao*, Andrea Marinoni, Bing Zhang, Kwok Po NG

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Spectral unmixing (SU) aims at decomposing the mixed pixel into basic components, called endmembers with corresponding abundance fractions. Linear mixing model (LMM) and nonlinear mixing models (NLMMs) are two main classes to solve the SU. This paper proposes a new nonlinear unmixing method base on general bilinear model, which is one of the NLMMs. Since retrieving the endmembers’ abundances represents an ill-posed inverse problem, prior knowledge of abundances has been investigated by conceiving regularizations techniques (e.g., sparsity, total variation, group sparsity, and low rankness), so to enhance the ability to restrict the solution space and thus to achieve reliable estimates. All the regularizations mentioned above can be interpreted as denoising of abundance maps. In this paper, instead of investing effort in designing more powerful regularizations of abundances, we use plug-and-play prior technique, that is to use directly a state-of-the-art denoiser, which is conceived to exploit the spatial correlation of abundance maps and nonlinear interaction maps. The numerical results in simulated data and real hyperspectral dataset show that the proposed method can improve the estimation of abundances dramatically compared with state-of-the-art nonlinear unmixing methods.

Original languageEnglish
Article number4117
Pages (from-to)1-20
Number of pages20
JournalRemote Sensing
Volume12
Issue number24
DOIs
Publication statusPublished - 2 Dec 2020

Scopus Subject Areas

  • Earth and Planetary Sciences(all)

User-Defined Keywords

  • Denoising
  • Hyperspectral imagery
  • Nonlinear unmixing
  • Plug-and-play

Fingerprint

Dive into the research topics of 'Hyperspectral nonlinear unmixing by using plug-and-play prior for abundance maps'. Together they form a unique fingerprint.

Cite this