{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T11:08:10Z","timestamp":1768820890286,"version":"3.49.0"},"reference-count":39,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T00:00:00Z","timestamp":1689638400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Second Tibetan Plateau Scientific Expedition and Research Program","award":["2019QZKK0608"],"award-info":[{"award-number":["2019QZKK0608"]}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research Program","award":["OF202304"],"award-info":[{"award-number":["OF202304"]}]},{"name":"Open Fund of State Key Laboratory of Remote Sensing Science and Beijing Engineering Research Center for Global Land Remote Sensing Products","award":["2019QZKK0608"],"award-info":[{"award-number":["2019QZKK0608"]}]},{"name":"Open Fund of State Key Laboratory of Remote Sensing Science and Beijing Engineering Research Center for Global Land Remote Sensing Products","award":["OF202304"],"award-info":[{"award-number":["OF202304"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Scholars have performed much research on reducing the redundancy of hyperspectral data. As a measure of the similarity between hyperspectral bands, structural similarity is used in band selection methods. However, existing structural similarity methods calculate all the structural similarity between bands, which leads to excessively long runtimes for these methods. Aiming to address this problem, this paper proposes a band subspace partition method and combines it with the SR-SSIM band selection method to obtain an improved band selection method: E-SR-SSIM. E-SR-SSIM consists of two parts: band subspace partition and band subspace band selection. In the first part, the hyperspectral dataset is divided into subdatasets corresponding to a number of subspaces. In the second part, a modified SR-SSIM method is used for all subdatasets to select the most representative band in each subdataset. The Indian Pines, Salinas Kennedy Space Center and Wuhan unmanned aerial vehicle-borne hyperspectral image LongKou public datasets are used to implement the experiment. The experiment uses random forest as the supervised classifier: 10% of each category sample is randomly selected as training data, and the remaining 90% is used as test data. The evaluation indicators selected in the experiment are overall accuracy, average accuracy, kappa and recall. The experimental results show that E-SR-SSIM can effectively reduce the runtime while ensuring classification compared with SR-SSIM, and quantitative proof that the band subspace partition reduces the calculated amount of structural similarity is obtained through a mathematical analysis. The improved band subspace partition method could partition a dataset more reasonably than the original band subspace partition method.<\/jats:p>","DOI":"10.3390\/rs15143596","type":"journal-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T00:54:01Z","timestamp":1689728041000},"page":"3596","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Improved SR-SSIM Band Selection Method Based on Band Subspace Partition"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7876-143X","authenticated-orcid":false,"given":"Tingrui","family":"Hu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1714-779X","authenticated-orcid":false,"given":"Peichao","family":"Gao","sequence":"additional","affiliation":[{"name":"Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8805-8914","authenticated-orcid":false,"given":"Sijing","family":"Ye","sequence":"additional","affiliation":[{"name":"Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9126-229X","authenticated-orcid":false,"given":"Shi","family":"Shen","sequence":"additional","affiliation":[{"name":"Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1007\/s11442-023-2080-3","article-title":"Exploring detailed urban-rural development under intersecting population growth and food production scenarios: Trajectories for China\u2019s most populous agricultural province to 2030","volume":"33","author":"Gao","year":"2023","journal-title":"J. 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