{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T08:52:20Z","timestamp":1765356740738,"version":"build-2065373602"},"reference-count":58,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2020,7,10]],"date-time":"2020-07-10T00:00:00Z","timestamp":1594339200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the State Key Program of National Natural Science of China","award":["61836009"],"award-info":[{"award-number":["61836009"]}]},{"name":"Project supported the Foundation for Innovative Research Groups of the National Natural Science Foundation of China","award":["61621005"],"award-info":[{"award-number":["61621005"]}]},{"name":"the Major Research Plan of the National Natural Science Foundation of China","award":["91438201, 91438103, 61801124"],"award-info":[{"award-number":["91438201, 91438103, 61801124"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1701267, 61772399, U170126, 61906150 and 61773304"],"award-info":[{"award-number":["U1701267, 61772399, U170126, 61906150 and 61773304"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the National Science Basic Research Plan in Shaanxi Province of China","award":["2019JQ659"],"award-info":[{"award-number":["2019JQ659"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High-resolution optical remote sensing image classification is an important research direction in the field of computer vision. It is difficult to extract the rich semantic information from remote sensing images with many objects. In this paper, a multiscale self-adaptive attention network (MSAA-Net) is proposed for the optical remote sensing image classification, which includes multiscale feature extraction, adaptive information fusion, and classification. In the first part, two parallel convolution blocks with different receptive fields are adopted to capture multiscale features. Then, the squeeze process is used to obtain global information and the excitation process is used to learn the weights in different channels, which can adaptively select useful information from multiscale features. Furthermore, the high-level features are classified by many residual blocks with an attention mechanism and a fully connected layer. Experiments were conducted using the UC Merced, NWPU, and the Google SIRI-WHU datasets. Compared to the state-of-the-art methods, the MSAA-Net has great effect and robustness, with average accuracies of 94.52%, 95.01%, and 95.21% on the three widely used remote sensing datasets.<\/jats:p>","DOI":"10.3390\/rs12142209","type":"journal-article","created":{"date-parts":[[2020,7,10]],"date-time":"2020-07-10T09:25:28Z","timestamp":1594373128000},"page":"2209","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Multiscale Self-Adaptive Attention Network for Remote Sensing Scene Classification"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6130-2518","authenticated-orcid":false,"given":"Lingling","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Pujiang","family":"Liang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Jingjing","family":"Ma","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Licheng","family":"Jiao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Xiaohui","family":"Guo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Fang","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Chen","family":"Sun","sequence":"additional","affiliation":[{"name":"The Fifth Electronics Research Institute of Ministry of Industry and Information Technology, Guangzhou 510610, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chen, W., Li, X., He, H., and Wang, L. 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