{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T20:28:10Z","timestamp":1783628890358,"version":"3.55.0"},"reference-count":33,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,7]],"date-time":"2022-08-07T00:00:00Z","timestamp":1659830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002767","name":"Hunan Provincial Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2020NK2033"],"award-info":[{"award-number":["2020NK2033"]}],"id":[{"id":"10.13039\/501100002767","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002767","name":"Hunan Provincial Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022JJ40189"],"award-info":[{"award-number":["2022JJ40189"]}],"id":[{"id":"10.13039\/501100002767","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Hyperspectral image (HSI) clustering is a challenging task, whose purpose is to assign each pixel to a corresponding cluster. The high-dimensionality and noise corruption are two main problems that limit the performance of HSI clustering. To address those problems, this paper proposes a projected clustering with a spatial\u2013spectral constrained adaptive graph (PCSSCAG) method for HSI clustering. PCSSCAG first constructs an adaptive adjacency graph to capture the accurate local geometric structure of HSI data adaptively. Then, a spatial\u2013spectral constraint is employed to simultaneously explore the spatial and spectral information for reducing the negative influence on graph construction caused by noise. Finally, projection learning is integrated into the spatial\u2013spectral constrained adaptive graph construction for reducing the redundancy and alleviating the computational cost. In addition, an alternating iteration algorithm is designed to solve the proposed model, and its computational complexity is theoretically analyzed. Experiments on two different scales of HSI datasets are conducted to evaluate the performance of PCSSCAG. The associated experimental results demonstrate the superiority of the proposed method for HSI clustering.<\/jats:p>","DOI":"10.3390\/s22155906","type":"journal-article","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T04:16:55Z","timestamp":1660018615000},"page":"5906","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Spatial\u2013Spectral Constrained Adaptive Graph for Hyperspectral Image Clustering"],"prefix":"10.3390","volume":"22","author":[{"given":"Xing-Hui","family":"Zhu","sequence":"first","affiliation":[{"name":"College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China"},{"name":"Hunan Provincial Engineering and Technology Research Center for Rural and Agricultural Informatization, Hunan Agricultural University, Changsha 410128, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China"},{"name":"Hunan Provincial Engineering and Technology Research Center for Rural and Agricultural Informatization, Hunan Agricultural University, Changsha 410128, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng-Long","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China"},{"name":"Hunan Provincial Engineering and Technology Research Center for Rural and Agricultural Informatization, Hunan Agricultural University, Changsha 410128, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang-Jun","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China"},{"name":"Hunan Provincial Engineering and Technology Research Center for Rural and Agricultural Informatization, Hunan Agricultural University, Changsha 410128, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/JSTARS.2013.2267204","article-title":"Progress in hyperspectral remote sensing science and technology in China over the past three decades","volume":"7","author":"Tong","year":"2014","journal-title":"IEEE J. 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