{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T03:31:10Z","timestamp":1762918270300,"version":"build-2065373602"},"reference-count":66,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T00:00:00Z","timestamp":1628208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing image scene classification (RSISC) has broad application prospects, but related challenges still exist and urgently need to be addressed. One of the most important challenges is how to learn a strong discriminative scene representation. Recently, convolutional neural networks (CNNs) have shown great potential in RSISC due to their powerful feature learning ability; however, their performance may be restricted by the complexity of remote sensing images, such as spatial layout, varying scales, complex backgrounds, category diversity, etc. In this paper, we propose an attention-guided multilayer feature aggregation network (AGMFA-Net) that attempts to improve the scene classification performance by effectively aggregating features from different layers. Specifically, to reduce the discrepancies between different layers, we employed the channel\u2013spatial attention on multiple high-level convolutional feature maps to capture more accurately semantic regions that correspond to the content of the given scene. Then, we utilized the learned semantic regions as guidance to aggregate the valuable information from multilayer convolutional features, so as to achieve stronger scene features for classification. Experimental results on three remote sensing scene datasets indicated that our approach achieved competitive classification performance in comparison to the baselines and other state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs13163113","type":"journal-article","created":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T08:01:42Z","timestamp":1628236902000},"page":"3113","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["An Attention-Guided Multilayer Feature Aggregation Network for Remote Sensing Image Scene Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1706-1956","authenticated-orcid":false,"given":"Ming","family":"Li","sequence":"first","affiliation":[{"name":"The College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Lin","family":"Lei","sequence":"additional","affiliation":[{"name":"The College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Yuqi","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]},{"given":"Yuli","family":"Sun","sequence":"additional","affiliation":[{"name":"The College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Gangyao","family":"Kuang","sequence":"additional","affiliation":[{"name":"The College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3639","DOI":"10.1109\/TGRS.2016.2636241","article-title":"Deep recurrent neural networks for hyperspectral image classification","volume":"55","author":"Mou","year":"2017","journal-title":"IEEE Trans. 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