{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T17:22:00Z","timestamp":1782408120116,"version":"3.54.5"},"reference-count":64,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,19]],"date-time":"2019-01-19T00:00:00Z","timestamp":1547856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61371152, 61771391"],"award-info":[{"award-number":["61371152, 61771391"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Municipal Science and Technology Innovation Committee","award":["JCYJ20170815162956949"],"award-info":[{"award-number":["JCYJ20170815162956949"]}]},{"name":"Fund for Scientific Research in Flanders (FWO) project","award":["G037115N"],"award-info":[{"award-number":["G037115N"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral image compressive sensing reconstruction (HSI-CSR) is an important issue in remote sensing, and has recently been investigated increasingly by the sparsity prior based approaches. However, most of the available HSI-CSR methods consider the sparsity prior in spatial and spectral vector domains via vectorizing hyperspectral cubes along a certain dimension. Besides, in most previous works, little attention has been paid to exploiting the underlying nonlocal structure in spatial domain of the HSI. In this paper, we propose a nonlocal tensor sparse and low-rank regularization (NTSRLR) approach, which can encode essential structured sparsity of an HSI and explore its advantages for HSI-CSR task. Specifically, we study how to utilize reasonably the     l 1    -based sparsity of core tensor and tensor nuclear norm function as tensor sparse and low-rank regularization, respectively, to describe the nonlocal spatial-spectral correlation hidden in an HSI. To study the minimization problem of the proposed algorithm, we design a fast implementation strategy based on the alternative direction multiplier method (ADMM) technique. Experimental results on various HSI datasets verify that the proposed HSI-CSR algorithm can significantly outperform existing state-of-the-art CSR techniques for HSI recovery.<\/jats:p>","DOI":"10.3390\/rs11020193","type":"journal-article","created":{"date-parts":[[2019,1,22]],"date-time":"2019-01-22T03:08:22Z","timestamp":1548126502000},"page":"193","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["Nonlocal Tensor Sparse Representation and Low-Rank Regularization for Hyperspectral Image Compressive Sensing Reconstruction"],"prefix":"10.3390","volume":"11","author":[{"given":"Jize","family":"Xue","sequence":"first","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6974-7327","authenticated-orcid":false,"given":"Yongqiang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Research &amp; Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518057, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2183-0324","authenticated-orcid":false,"given":"Wenzhi","family":"Liao","sequence":"additional","affiliation":[{"name":"Department of Telecommunications and Information Processing, Ghent University-TELIN-IMEC, 9000 Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan Cheung-Wai","family":"Chan","sequence":"additional","affiliation":[{"name":"Department of Electronics and Informatics, Vrije, Universiteit Brussel, 1050 Brussel, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4729","DOI":"10.1109\/TGRS.2017.2698503","article-title":"Learning and transferring deep joint spectral\u2014Spatial features for hyperspectral classification","volume":"55","author":"Yang","year":"2017","journal-title":"IEEE Trans. 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