{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T19:24:39Z","timestamp":1766690679630,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,5,1]],"date-time":"2019-05-01T00:00:00Z","timestamp":1556668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Basic and Frontier Research Programmes of Chongqing","award":["cstc2018jcyjAX0093"],"award-info":[{"award-number":["cstc2018jcyjAX0093"]}]},{"name":"the Chongqing 325 University Postgraduates Innovation Project","award":["CYB18048 and CYS18035"],"award-info":[{"award-number":["CYB18048 and CYS18035"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41371338"],"award-info":[{"award-number":["41371338"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Many graph embedding methods are developed for dimensionality reduction (DR) of hyperspectral image (HSI), which only use spectral features to reflect a point-to-point intrinsic relation and ignore complex spatial-spectral structure in HSI. A new DR method termed spatial-spectral regularized sparse hypergraph embedding (SSRHE) is proposed for the HSI classification. SSRHE explores sparse coefficients to adaptively select neighbors for constructing the dual sparse hypergraph. Based on the spatial coherence property of HSI, a local spatial neighborhood scatter is computed to preserve local structure, and a total scatter is computed to represent the global structure of HSI. Then, an optimal discriminant projection is obtained by possessing better intraclass compactness and interclass separability, which is beneficial for classification. Experiments on Indian Pines and PaviaU hyperspectral datasets illustrated that SSRHE effectively develops a better classification performance compared with the traditional spectral DR algorithms.<\/jats:p>","DOI":"10.3390\/rs11091039","type":"journal-article","created":{"date-parts":[[2019,5,2]],"date-time":"2019-05-02T03:15:22Z","timestamp":1556766922000},"page":"1039","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Dimensionality Reduction of Hyperspectral Image Using Spatial-Spectral Regularized Sparse Hypergraph Embedding"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7377-3077","authenticated-orcid":false,"given":"Hong","family":"Huang","sequence":"first","affiliation":[{"name":"Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing 400044, China"}]},{"given":"Meili","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing 400044, China"}]},{"given":"Yule","family":"Duan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing 400044, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1109\/JSTARS.2017.2775644","article-title":"Constrained manifold learning for hyperspectral imagery visualization","volume":"1","author":"Liao","year":"2018","journal-title":"IEEE J. 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