{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:05:03Z","timestamp":1760241903129,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,10,16]],"date-time":"2018-10-16T00:00:00Z","timestamp":1539648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["No. 61502206, 61601236, 61772277, 61701238, 61772274 and 61471199"],"award-info":[{"award-number":["No. 61502206, 61601236, 61772277, 61701238, 61772274 and 61471199"]}]},{"name":"Nature Science Foundation of Jiangsu Province","award":["No. BK20150523, 20150923, BK20171494, BK20170858"],"award-info":[{"award-number":["No. BK20150523, 20150923, BK20171494, BK20170858"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["No. 30917015104"],"award-info":[{"award-number":["No. 30917015104"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High dimensional image classification is a fundamental technique for information retrieval from hyperspectral remote sensing data. However, data quality is readily affected by the atmosphere and noise in the imaging process, which makes it difficult to achieve good classification performance. In this paper, multiple kernel learning-based low rank representation at superpixel level (Sp_MKL_LRR) is proposed to improve the classification accuracy for hyperspectral images. Superpixels are generated first from the hyperspectral image to reduce noise effect and form homogeneous regions. An optimal superpixel kernel parameter is then selected by the kernel matrix using a multiple kernel learning framework. Finally, a kernel low rank representation is applied to classify the hyperspectral image. The proposed method offers two advantages. (1) The global correlation constraint is exploited by the low rank representation, while the local neighborhood information is extracted as the superpixel kernel adaptively learns the high-dimensional manifold features of the samples in each class; (2) It can meet the challenges of multiscale feature learning and adaptive parameter determination in the conventional kernel methods. Experimental results on several hyperspectral image datasets demonstrate that the proposed method outperforms several state-of-the-art classifiers tested in terms of overall accuracy, average accuracy, and kappa statistic.<\/jats:p>","DOI":"10.3390\/rs10101639","type":"journal-article","created":{"date-parts":[[2018,10,16]],"date-time":"2018-10-16T11:07:51Z","timestamp":1539688071000},"page":"1639","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Hyperspectral Classification via Superpixel Kernel Learning-Based Low Rank Representation"],"prefix":"10.3390","volume":"10","author":[{"given":"Tianming","family":"Zhan","sequence":"first","affiliation":[{"name":"School of Information and Technology, Nanjing Audit University, Nanjing 211815, China"},{"name":"Jiangsu Key Laboratory of Auditing Information Engineering, Nanjing 211815, China"}]},{"given":"Le","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Yang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Guowei","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information and Technology, Nanjing Audit University, Nanjing 211815, China"},{"name":"Jiangsu Key Laboratory of Auditing Information Engineering, Nanjing 211815, China"}]},{"given":"Yan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Lianyungang E-Port Information Development Co., Ltd., Lianyungang 222042, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7162-0202","authenticated-orcid":false,"given":"Zebin","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"},{"name":"Lianyungang E-Port Information Development Co., Ltd., Lianyungang 222042, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,16]]},"reference":[{"key":"ref_1","first-page":"287","article-title":"Hyperspectral remote sensing applied to mineral exploration in southern Peru: A multiple data integration approach in the Chapi Chiara gold prospect","volume":"64","author":"Carrino","year":"2018","journal-title":"Int. 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