{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:05:00Z","timestamp":1778346300641,"version":"3.51.4"},"reference-count":53,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62406250"],"award-info":[{"award-number":["62406250"]}]},{"name":"National Natural Science Foundation of China","award":["2023-JC-QN-0702"],"award-info":[{"award-number":["2023-JC-QN-0702"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["62406250"],"award-info":[{"award-number":["62406250"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2023-JC-QN-0702"],"award-info":[{"award-number":["2023-JC-QN-0702"]}]},{"name":"Key Laboratory of Intelligent Equipment Application, Ministry of Education, Rocket Force University of Engineering","award":["62406250"],"award-info":[{"award-number":["62406250"]}]},{"name":"Key Laboratory of Intelligent Equipment Application, Ministry of Education, Rocket Force University of Engineering","award":["2023-JC-QN-0702"],"award-info":[{"award-number":["2023-JC-QN-0702"]}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["62406250"],"award-info":[{"award-number":["62406250"]}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2023-JC-QN-0702"],"award-info":[{"award-number":["2023-JC-QN-0702"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recently, deep learning models have been successfully and widely applied in the field of remote sensing scene classification. But, the existing deep models largely overlook the distinct learning difficulties associated with discriminating different pairs of scenes. Consequently, leveraging the relationships within category distributions and employing ensemble learning algorithms hold considerable potential in addressing these issues. In this paper, we propose a category-distribution-associated deep ensemble learning model that pays more attention to instances that are difficult to identify between similar scenes. The core idea is to utilize the degree of difficulty between categories to guide model learning, which is primarily divided into two modules: category distribution information extraction and scene classification. This method employs an autoencoder to capture distinct scene distributions within the samples and constructs a similarity matrix based on the discrepancies between distributions. Subsequently, the scene classification module adopts a stacking ensemble framework, where the base layer utilizes various neural networks to capture sample representations from shallow to deep levels. The meta layer incorporates a novel multiclass boosting algorithm that integrates sample distribution and representations of information to discriminate scenes. Exhaustive empirical evaluations on remote sensing scene benchmarks demonstrate the effectiveness and superiority of our proposed method over the state-of-the-art approaches.<\/jats:p>","DOI":"10.3390\/rs16214084","type":"journal-article","created":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T09:49:14Z","timestamp":1730454554000},"page":"4084","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Deep Ensemble Remote Sensing Scene Classification via Category Distribution Association"],"prefix":"10.3390","volume":"16","author":[{"given":"Zhenxin","family":"He","sequence":"first","affiliation":[{"name":"Xi\u2019an Research Institute of High Technology, Xi\u2019an 710025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoxu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guanxiong","family":"He","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Mathematica Sciences, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9240-6726","authenticated-orcid":false,"given":"Rong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,1]]},"reference":[{"key":"ref_1","first-page":"13","article-title":"A Two-Stream Deep Fusion Framework for High-Resolution Aerial Scene Classification","volume":"18","author":"Yu","year":"2018","journal-title":"Comput. 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