{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:24:31Z","timestamp":1760232271775,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,27]],"date-time":"2022-10-27T00:00:00Z","timestamp":1666828800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"French Government, through the program Investments for the Future managed by the National Agency for Research","award":["ANR-16-IDEX-0007"],"award-info":[{"award-number":["ANR-16-IDEX-0007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A hyperspectral image provides fine details about the scene under analysis, due to its multiple bands. However, the resulting high dimensionality in the feature space may render a classification task unreliable, mainly due to overfitting and the Hughes phenomenon. In order to attenuate such problems, one can resort to dimensionality reduction (DR). Thus, this paper proposes a new DR algorithm, which performs an unsupervised band selection technique following a clustering approach. More specifically, the data set was split into a predefined number of clusters, after which the bands were iteratively selected based on the parameters of a separating hyperplane, which provided the best separation in the feature space, in a one-versus-all scenario. Then, a fine-tuning of the initially selected bands took place based on the separability of clusters. A comparison with five other state-of-the-art frameworks shows that the proposed method achieved the best classification results in 60% of the experiments.<\/jats:p>","DOI":"10.3390\/rs14215374","type":"journal-article","created":{"date-parts":[[2022,10,27]],"date-time":"2022-10-27T04:35:17Z","timestamp":1666845317000},"page":"5374","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Unsupervised Cluster-Wise Hyperspectral Band Selection for Classification"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5701-0558","authenticated-orcid":false,"given":"Mateus","family":"Habermann","sequence":"first","affiliation":[{"name":"Institute for Advanced Studies, Sao Jose dos Campos 12228-001, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5226-0435","authenticated-orcid":false,"given":"Elcio Hideiti","family":"Shiguemori","sequence":"additional","affiliation":[{"name":"Institute for Advanced Studies, Sao Jose dos Campos 12228-001, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1627-2241","authenticated-orcid":false,"given":"Vincent","family":"Fr\u00e9mont","sequence":"additional","affiliation":[{"name":"Department of Automatics and Robotics, Nantes Universit\u00e9, \u00c9cole Centrale de Nantes, CNRS, LS2N, UMR 6004, F-44000 Nantes, France"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,27]]},"reference":[{"key":"ref_1","unstructured":"Theodoridis, S., and Koutroumbas, K. 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