{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:41:43Z","timestamp":1760240503577,"version":"build-2065373602"},"reference-count":63,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2019,6,29]],"date-time":"2019-06-29T00:00:00Z","timestamp":1561766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003329","name":"Ministerio de Econom\u00eda y Competitividad","doi-asserted-by":"publisher","award":["TIN2015-63646-C5-1-R"],"award-info":[{"award-number":["TIN2015-63646-C5-1-R"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010801","name":"Xunta de Galicia","doi-asserted-by":"publisher","award":["ED431C 2017\/12"],"award-info":[{"award-number":["ED431C 2017\/12"]}],"id":[{"id":"10.13039\/501100010801","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper addresses three problems in the field of hyperspectral image segmentation: the fact that the way an image must be segmented is related to what the user requires and the application; the lack and cost of appropriately labeled reference images; and, finally, the information loss problem that arises in many algorithms when high dimensional images are projected onto lower dimensional spaces before starting the segmentation process. To address these issues, the Multi-Gradient based Cellular Automaton (MGCA) structure is proposed to segment multidimensional images without projecting them to lower dimensional spaces. The MGCA structure is coupled with an evolutionary algorithm (ECAS-II) in order to produce the transition rule sets required by MGCA segmenters. These sets are customized to specific segmentation needs as a function of a set of low dimensional training images in which the user expresses his segmentation requirements. Constructing high dimensional image segmenters from low dimensional training sets alleviates the problem of lack of labeled training images. These can be generated online based on a parametrization of the desired segmentation extracted from a set of examples. The strategy has been tested in experiments carried out using synthetic and real hyperspectral images, and it has been compared to state-of-the-art segmentation approaches over benchmark images in the area of remote sensing hyperspectral imaging.<\/jats:p>","DOI":"10.3390\/s19132887","type":"journal-article","created":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T03:23:59Z","timestamp":1561951439000},"page":"2887","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["An Approach for the Customized High-Dimensional Segmentation of Remote Sensing Hyperspectral Images"],"prefix":"10.3390","volume":"19","author":[{"given":"Blanca","family":"Priego","sequence":"first","affiliation":[{"name":"Biomedical Engineering and Telemedicine Researching Group, University of C\u00e1diz, 11002 C\u00e1diz, Spain"},{"name":"Institute of Research and Innovation in Biomedical Sciences of the Province of C\u00e1diz (INiBICA), University of C\u00e1diz, 11002 C\u00e1diz, Spain"}]},{"given":"Richard J.","family":"Duro","sequence":"additional","affiliation":[{"name":"Integrated Group for Engineering Research, Centro de Investigaci\u00f3n en Tecnolog\u00edas de la Informaci\u00f3n y las Comunicaciones (CITIC), Universidade da Coru\u00f1a, 15403 Ferrol, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","article-title":"On the mean accuracy of statistical pattern recognizers","volume":"14","author":"Hughes","year":"1968","journal-title":"IEEE Trans. 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