{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,7]],"date-time":"2025-12-07T03:36:12Z","timestamp":1765078572919,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,28]],"date-time":"2021-07-28T00:00:00Z","timestamp":1627430400000},"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":["42001307","62061038"],"award-info":[{"award-number":["42001307","62061038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ningxia Key RD Program","award":["2020BFG02013"],"award-info":[{"award-number":["2020BFG02013"]}]},{"DOI":"10.13039\/501100004772","name":"Natural Science Foundation of Ningxia Province","doi-asserted-by":"publisher","award":["2020AAC02006"],"award-info":[{"award-number":["2020AAC02006"]}],"id":[{"id":"10.13039\/501100004772","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In the recent years, convolutional neural networks (CNN)-based super resolution (SR) methods are widely used in the field of remote sensing. However, complicated remote sensing images contain abundant high-frequency details, which are difficult to capture and reconstruct effectively. To address this problem, we propose a dense channel attention network (DCAN) to reconstruct high-resolution (HR) remote sensing images. The proposed method learns multi-level feature information and pays more attention to the important and useful regions in order to better reconstruct the final image. Specifically, we construct a dense channel attention mechanism (DCAM), which densely uses the feature maps from the channel attention block via skip connection. This mechanism makes better use of multi-level feature maps which contain abundant high-frequency information. Further, we add a spatial attention block, which makes the network have more flexible discriminative ability. Experimental results demonstrate that the proposed DCAN method outperforms several state-of-the-art methods in both quantitative evaluation and visual quality.<\/jats:p>","DOI":"10.3390\/rs13152966","type":"journal-article","created":{"date-parts":[[2021,7,28]],"date-time":"2021-07-28T21:21:04Z","timestamp":1627507264000},"page":"2966","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Remote Sensing Image Super-Resolution Based on Dense Channel Attention Network"],"prefix":"10.3390","volume":"13","author":[{"given":"Yunchuan","family":"Ma","sequence":"first","affiliation":[{"name":"School of Information Engineering, Ningxia University, Yinchuan 750021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8394-0078","authenticated-orcid":false,"given":"Pengyuan","family":"Lv","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Ningxia University, Yinchuan 750021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0954-5405","authenticated-orcid":false,"given":"Hao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Ningxia University, Yinchuan 750021, China"}]},{"given":"Xuehong","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Ningxia University, Yinchuan 750021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9446-5850","authenticated-orcid":false,"given":"Yanfei","family":"Zhong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2104","DOI":"10.1109\/TGRS.2019.2953119","article-title":"Object detection in high resolution remote sensing imagery based on convolutional neural networks with suitable object scale features","volume":"58","author":"Dong","year":"2019","journal-title":"IEEE Trans. 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