{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T04:31:33Z","timestamp":1775709093726,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T00:00:00Z","timestamp":1682467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research Program of Frontier Sciences, CAS","award":["ZDBS-LY-DQC016"],"award-info":[{"award-number":["ZDBS-LY-DQC016"]}]},{"name":"Key Research Program of Frontier Sciences, CAS","award":["2022YFF1301803"],"award-info":[{"award-number":["2022YFF1301803"]}]},{"name":"Key Research Program of Frontier Sciences, CAS","award":["62076242"],"award-info":[{"award-number":["62076242"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["ZDBS-LY-DQC016"],"award-info":[{"award-number":["ZDBS-LY-DQC016"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFF1301803"],"award-info":[{"award-number":["2022YFF1301803"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["62076242"],"award-info":[{"award-number":["62076242"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["ZDBS-LY-DQC016"],"award-info":[{"award-number":["ZDBS-LY-DQC016"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["2022YFF1301803"],"award-info":[{"award-number":["2022YFF1301803"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["62076242"],"award-info":[{"award-number":["62076242"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Semantic segmentation of remote-sensing (RS) images is one of the most fundamental tasks in the understanding of a remote-sensing scene. However, high-resolution RS images contain plentiful detailed information about ground objects, which scatter everywhere spatially and have variable sizes, styles, and visual appearances. Due to the high similarity between classes and diversity within classes, it is challenging to obtain satisfactory and accurate semantic segmentation results. This paper proposes a Dynamic High-Resolution Network (DyHRNet) to solve this problem. Our proposed network takes HRNet as a super-architecture, aiming to leverage the important connections and channels by further investigating the parallel streams at different resolution representations of the original HRNet. The learning task is conducted under the framework of a neural architecture search (NAS) and channel-wise attention module. Specifically, the Accelerated Proximal Gradient (APG) algorithm is introduced to iteratively solve the sparse regularization subproblem from the perspective of neural architecture search. In this way, valuable connections are selected for cross-resolution feature fusion. In addition, a channel-wise attention module is designed to weight the channel contributions for feature aggregation. Finally, DyHRNet fully realizes the dynamic advantages of data adaptability by combining the APG algorithm and channel-wise attention module simultaneously. Compared with nine classical or state-of-the-art models (FCN, UNet, PSPNet, DeepLabV3+, OCRNet, SETR, SegFormer, HRNet+FCN, and HRNet+OCR), DyHRNet has shown high performance on three public challenging RS image datasets (Vaihingen, Potsdam, and LoveDA). Furthermore, the visual segmentation results, the learned structures, the iteration process analysis, and the ablation study all demonstrate the effectiveness of our proposed model.<\/jats:p>","DOI":"10.3390\/rs15092293","type":"journal-article","created":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T01:28:28Z","timestamp":1682558908000},"page":"2293","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Dynamic High-Resolution Network for Semantic Segmentation in Remote-Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Shichen","family":"Guo","sequence":"first","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Qi","family":"Yang","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2089-9733","authenticated-orcid":false,"given":"Shiming","family":"Xiang","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"}]},{"given":"Pengfei","family":"Wang","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China"}]},{"given":"Xuezhi","family":"Wang","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2017.12.007","article-title":"Semantic Labeling in very High Resolution Images via A Self-cascaded Convolutional Neural Network","volume":"145","author":"Liu","year":"2018","journal-title":"ISPRS J. 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