{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T02:17:49Z","timestamp":1768270669685,"version":"3.49.0"},"reference-count":39,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2017,12,5]],"date-time":"2017-12-05T00:00:00Z","timestamp":1512432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Agencia Estatal de Invetigaci\u00f3n (AEI) and Fondo Europeo de Desarrollo Regional (FEDER)","award":["CTM2016-77733-R"],"award-info":[{"award-number":["CTM2016-77733-R"]}]},{"DOI":"10.13039\/501100003329","name":"Ministerio de Econom\u00eda y Competitividad","doi-asserted-by":"publisher","award":["BES-2014-069426"],"award-info":[{"award-number":["BES-2014-069426"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Hyperspectral imagery (HSI) integrates many continuous and narrow bands that cover different regions of the electromagnetic spectrum. However, the main challenge is the high dimensionality of HSI data due to the \u2019Hughes\u2019 phenomenon. Thus, dimensionality reduction is necessary before applying classification algorithms to obtain accurate thematic maps. We focus the study on the following feature-extraction algorithms: Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA). After a literature survey, we have observed a lack of a comparative study on these techniques as well as accurate strategies to determine the number of components. Hence, the first objective was to compare traditional dimensionality reduction techniques (PCA, MNF, and ICA) in HSI of the Compact Airborne Spectrographic Imager (CASI) sensor and to evaluate different strategies for selecting the most suitable number of components in the transformed space. The second objective was to determine a new dimensionality reduction approach by dividing the CASI HSI regarding the spectral regions covering the electromagnetic spectrum. The components selected from the transformed space of the different spectral regions were stacked. This stacked transformed space was evaluated to see if the proposed approach improves the final classification.<\/jats:p>","DOI":"10.3390\/e19120666","type":"journal-article","created":{"date-parts":[[2017,12,5]],"date-time":"2017-12-05T11:50:28Z","timestamp":1512474628000},"page":"666","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Assessment of Component Selection Strategies in Hyperspectral Imagery"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5062-7491","authenticated-orcid":false,"given":"Edurne","family":"Ibarrola-Ulzurrun","sequence":"first","affiliation":[{"name":"Instituto de Oceanograf\u00eda y Cambio Global, IOCAG, Universidad de Las Palmas de Gran Canaria, ULPGC, Parque Cient\u00edfico Tecnol\u00f3gico Marino de Taliarte, s\/n, 35214 Telde, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9646-1017","authenticated-orcid":false,"given":"Javier","family":"Marcello","sequence":"additional","affiliation":[{"name":"Instituto de Oceanograf\u00eda y Cambio Global, IOCAG, Universidad de Las Palmas de Gran Canaria, ULPGC, Parque Cient\u00edfico Tecnol\u00f3gico Marino de Taliarte, s\/n, 35214 Telde, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0804-9293","authenticated-orcid":false,"given":"Consuelo","family":"Gonzalo-Martin","sequence":"additional","affiliation":[{"name":"Center of Biomedical Technology, Universidad Polit\u00e9cnica de Madrid, UPM, Campus de Montegancedo, Pozuelo de Alarc\u00f3n, 28223 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,12,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1109\/MSP.2014.2312071","article-title":"Effective feature extraction and data reduction in remote sensing using hyperspectral imaging","volume":"31","author":"Ren","year":"2014","journal-title":"IEEE Signal Process. 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