{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:59:46Z","timestamp":1753887586838,"version":"3.41.2"},"reference-count":18,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T00:00:00Z","timestamp":1618444800000},"content-version":"vor","delay-in-days":104,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61906097"],"award-info":[{"award-number":["61906097"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>A ReliefF improved mRMR (RmRMR) criterion\u2010based bag of visual words (BoVW) algorithm is proposed to filter the visual words that are generated with high information redundancy for remote sensing image classification. First, the contribution degree of each word to the classification is represented by its weighting parameter, which is assigned using the ReliefF algorithm. Next, the relevance and redundancy of each word are calculated according to the mRMR criterion with the addition of a dictionary balance coefficient. Finally, a novel dictionary discriminant function is established, and the globally discriminative small\u2010scale dictionary subsets are filtered and obtained. Experimental results show that the proposed algorithm effectively reduces the amount of redundant information in the dictionary and better balances the relevance and redundancy of words to improve the feature descriptive power of dictionary subsets and markedly increase the classification precision on a high\u2010resolution remote sensing image.<\/jats:p>","DOI":"10.1155\/2021\/7589481","type":"journal-article","created":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T19:19:28Z","timestamp":1618514368000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["High\u2010Resolution Remote Sensing Image Classification with RmRMR\u2010Enhanced Bag of Visual Words"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0673-7754","authenticated-orcid":false,"given":"Suting","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3863-1177","authenticated-orcid":false,"given":"Liangchen","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Chuang","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,4,15]]},"reference":[{"key":"e_1_2_9_1_2","first-page":"236","article-title":"Progress in hyperspectral remote sensing image classification","volume":"02","author":"Du P.","year":"2016","journal-title":"Journal of Remote Sensing"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1080\/01431160600746456"},{"key":"e_1_2_9_3_2","first-page":"38","article-title":"On the target classification method based on weak supervised E2LSH and significant graph weighting","volume":"38","author":"Zhao Y.","year":"2016","journal-title":"Journal of Electronics & Information Technology"},{"key":"e_1_2_9_4_2","doi-asserted-by":"crossref","unstructured":"YangL. 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