{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T19:35:46Z","timestamp":1772825746925,"version":"3.50.1"},"reference-count":88,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,17]],"date-time":"2022-03-17T00:00:00Z","timestamp":1647475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Science and Technology Foundation of Education Department of Jiangxi Province, China.","award":["GJJ203204"],"award-info":[{"award-number":["GJJ203204"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Discriminative feature learning is the key to remote sensing scene classification. Previous research has found that most of the existing convolutional neural networks (CNN) focus on the global semantic features and ignore shallower features (low-level and middle-level features). This study proposes a novel Lie Group deep learning model for remote sensing scene classification to solve the above-mentioned challenges. Firstly, we extract shallower and higher-level features from images based on Lie Group machine learning (LGML) and deep learning to improve the feature representation ability of the model. In addition, a parallel dilated convolution, a kernel decomposition, and a Lie Group kernel function are adopted to reduce the model\u2019s parameters to prevent model degradation and over-fitting caused by the deepening of the model. Then, the spatial attention mechanism can enhance local semantic features and suppress irrelevant feature information. Finally, feature-level fusion is adopted to reduce redundant features and improve computational performance, and cross-entropy loss function based on label smoothing is used to improve the classification accuracy of the model. Comparative experiments on three public and challenging large-scale remote-sensing datasets show that our model improves the discriminative ability of features and achieves competitive accuracy against other state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs14061445","type":"journal-article","created":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T21:37:17Z","timestamp":1647812237000},"page":"1445","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A Combination of Lie Group Machine Learning and Deep Learning for Remote Sensing Scene Classification Using Multi-Layer Heterogeneous Feature Extraction and Fusion"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2052-7419","authenticated-orcid":false,"given":"Chengjun","family":"Xu","sequence":"first","affiliation":[{"name":"School of Software, Jiangxi Normal University, Nanchang 330022, China"},{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China"}]},{"given":"Guobin","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China"}]},{"given":"Jingqian","family":"Shu","sequence":"additional","affiliation":[{"name":"School of Software, Jiangxi Normal University, Nanchang 330022, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1109\/JSTARS.2020.3005403","article-title":"Remote sensing image scene classification meets deep learning: Challenges, methods, benchmarks, and opportunities","volume":"13","author":"Cheng","year":"2020","journal-title":"IEEE J. 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