{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T05:29:42Z","timestamp":1736314182666,"version":"3.32.0"},"reference-count":25,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T00:00:00Z","timestamp":1635465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41974108"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research Program of the Chinese Academy of Science","award":["KFZD-SW-317"]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA 19080101","XDA19080103"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"The detection of primary and secondary schools (PSSs) is a meaningful task for composite object detection in remote sensing images (RSIs). As a typical composite object in RSIs, PSSs have diverse appearances with complex backgrounds, which makes it difficult to effectively extract their features using the existing deep-learning-based object detection algorithms. Aiming at the challenges of PSSs detection, we propose an end-to-end framework called the attention-guided dense network (ADNet), which can effectively improve the detection accuracy of PSSs. First, a dual attention module (DAM) is designed to enhance the ability in representing complex characteristics and alleviate distractions in the background. Second, a dense feature fusion module (DFFM) is built to promote attention cues flow into low layers, which guides the generation of hierarchical feature representation. Experimental results demonstrate that our proposed method outperforms the state-of-the-art methods and achieves 79.86% average precision. The study proves the effectiveness of our proposed method on PSSs detection.<\/jats:p>","DOI":"10.3390\/ijgi10110736","type":"journal-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T02:21:34Z","timestamp":1635819694000},"page":"736","source":"Crossref","is-referenced-by-count":2,"title":["Detection of Schools in Remote Sensing Images Based on Attention-Guided Dense Network"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3587-0455","authenticated-orcid":false,"given":"Han","family":"Fu","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Xiangtao","family":"Fan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Zhenzhen","family":"Yan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0618-0984","authenticated-orcid":false,"given":"Xiaoping","family":"Du","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. 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