{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T05:19:02Z","timestamp":1771046342416,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2016,7,15]],"date-time":"2016-07-15T00:00:00Z","timestamp":1468540800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 41171323)"],"award-info":[{"award-number":["No. 41171323)"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Scientific 289 Instrument and Equipment Development Program","award":["No. 012YQ050250"],"award-info":[{"award-number":["No. 012YQ050250"]}]},{"DOI":"10.13039\/501100004608","name":"Jiangsu Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["No. BK2012018"],"award-info":[{"award-number":["No. BK2012018"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Kernel-based methods and ensemble learning are two important paradigms for the classification of hyperspectral remote sensing images. However, they were developed in parallel with different principles. In this paper, we aim to combine the advantages of kernel and ensemble methods by proposing a kernel supervised ensemble classification method. In particular, the proposed method, namely RoF-KOPLS, combines the merits of ensemble feature learning (i.e., Rotation Forest (RoF)) and kernel supervised learning (i.e., Kernel Orthonormalized Partial Least Square (KOPLS)). In particular, the feature space is randomly split into K disjoint subspace and KOPLS is applied to each subspace to produce the new features set for the training of decision tree classifier. The final classification result is assigned to the corresponding class by the majority voting rule. Experimental results on two hyperspectral airborne images demonstrated that RoF-KOPLS with radial basis function (RBF) kernel yields the best classification accuracies due to the ability of improving the accuracies of base classifiers and the diversity within the ensemble, especially for the very limited training set. Furthermore, our proposed method is insensitive to the number of subsets.<\/jats:p>","DOI":"10.3390\/rs8070601","type":"journal-article","created":{"date-parts":[[2016,7,15]],"date-time":"2016-07-15T11:08:05Z","timestamp":1468580885000},"page":"601","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Kernel Supervised Ensemble Classifier for the Classification of Hyperspectral Data Using Few Labeled Samples"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1929-987X","authenticated-orcid":false,"given":"Jike","family":"Chen","sequence":"first","affiliation":[{"name":"Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, 210093 Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junshi","family":"Xia","sequence":"additional","affiliation":[{"name":"Int\u00e9gration, du Mat\u00e9riau au Syst\u00e8me (IMS), Univsit\u00e9 de Bordeaux, UMR 5218, F-33405 Talence, France"},{"name":"Int\u00e9gration, du Mat\u00e9riau au Syst\u00e8me (IMS), Centre National de la Recherche Scientifique (CNRS), UMR 5218, F-33405 Talence, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peijun","family":"Du","sequence":"additional","affiliation":[{"name":"Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, 210093 Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4817-2875","authenticated-orcid":false,"given":"Jocelyn","family":"Chanussot","sequence":"additional","affiliation":[{"name":"Grenoble-Image-sPeech-Signal-Automatics Lab (GIPSA)-lab, Grenoble Institute of Technology, 38400 Grenoble, France"},{"name":"Faculty of Electrical and Computer Engineering, University of Iceland, 101 Reykjavik, Iceland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6253-2967","authenticated-orcid":false,"given":"Zhaohui","family":"Xue","sequence":"additional","affiliation":[{"name":"Department of Geomatics, Hohai University, 8 West of Focheng Road, 211100 Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangjian","family":"Xie","sequence":"additional","affiliation":[{"name":"Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, 210093 Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,7,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S110","DOI":"10.1016\/j.rse.2007.07.028","article-title":"Recent advances in techniques for hyperspectral image processing","volume":"113","author":"Plaza","year":"2009","journal-title":"Remote Sens. 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