{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:46:35Z","timestamp":1775144795221,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,16]],"date-time":"2020-12-16T00:00:00Z","timestamp":1608076800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China under Grant Research Fund","award":["no.41722108"],"award-info":[{"award-number":["no.41722108"]}]},{"name":"National Natural Science Foundation of China under Grant Research Fund","award":["no.42001287"],"award-info":[{"award-number":["no.42001287"]}]},{"name":"Centre for Integrated Remote Sensing and Forecasting for Arctic Operations (CIRFA) and the 295 Research Council of Norway","award":["no. 237906"],"award-info":[{"award-number":["no. 237906"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2019 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.<\/jats:p>","DOI":"10.3390\/rs12244117","type":"journal-article","created":{"date-parts":[[2020,12,16]],"date-time":"2020-12-16T22:12:06Z","timestamp":1608156726000},"page":"4117","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Hyperspectral Nonlinear Unmixing by Using Plug-and-Play Prior for Abundance Maps"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7604-3037","authenticated-orcid":false,"given":"Zhicheng","family":"Wang","sequence":"first","affiliation":[{"name":"The Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"The School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Lina","family":"Zhuang","sequence":"additional","affiliation":[{"name":"The Department of Mathematics, Hong Kong Baptist University, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3888-8124","authenticated-orcid":false,"given":"Lianru","family":"Gao","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Andrea","family":"Marinoni","sequence":"additional","affiliation":[{"name":"The Department of Physics and Technology, UiT The Arctic University of Norway, NO-9037 Troms\u00f8, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0319-7753","authenticated-orcid":false,"given":"Bing","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"The School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6833-5227","authenticated-orcid":false,"given":"Michael K.","family":"Ng","sequence":"additional","affiliation":[{"name":"The Department of Mathematics, The University of Hong Kong, Hong Kong, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MSP.2013.2279274","article-title":"Nonlinear Unmixing of Hyperspectral Images: Models and Algorithms","volume":"31","author":"Dobigeon","year":"2014","journal-title":"IEEE Signal Process. 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