{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:27:53Z","timestamp":1760232473220,"version":"build-2065373602"},"reference-count":64,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T00:00:00Z","timestamp":1667347200000},"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":["62071104","U2133211"],"award-info":[{"award-number":["62071104","U2133211"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NNSFC","award":["62071104","U2133211"],"award-info":[{"award-number":["62071104","U2133211"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Scene parsing of high-resolution remote-sensing images (HRRSIs) refers to parsing different semantic regions from the images, which is an important fundamental task in image understanding. However, due to the inherent complexity of urban scenes, HRRSIs contain numerous object classes. These objects present large-scale variation and irregular morphological structures. Furthermore, their spatial distribution is uneven and contains substantial spatial details. All these features make it difficult to parse urban scenes accurately. To deal with these dilemmas, in this paper, we propose a multi-branch adaptive hard region mining network (MBANet) for urban scene parsing of HRRSIs. MBANet consists of three branches, namely, a multi-scale semantic branch, an adaptive hard region mining (AHRM) branch, and an edge branch. First, the multi-scale semantic branch is constructed based on a feature pyramid network (FPN). To reduce the memory footprint, ResNet50 is chosen as the backbone, which, combined with the atrous spatial pyramid pooling module, can extract rich multi-scale contextual information effectively, thereby enhancing object representation at various scales. Second, an AHRM branch is proposed to enhance feature representation of hard regions with a complex distribution, which would be difficult to parse otherwise. Third, the edge-extraction branch is introduced to supervise boundary perception training so that the contours of objects can be better captured. In our experiments, the three branches complemented each other in feature extraction and demonstrated state-of-the-art performance for urban scene parsing of HRRSIs. We also performed ablation studies on two HRRSI datasets from ISPRS and compared them with other methods.<\/jats:p>","DOI":"10.3390\/rs14215527","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T03:53:07Z","timestamp":1667447587000},"page":"5527","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Multi-Branch Adaptive Hard Region Mining Network for Urban Scene Parsing of High-Resolution Remote-Sensing Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0895-4963","authenticated-orcid":false,"given":"Haiwei","family":"Bai","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic and Science Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6966-0531","authenticated-orcid":false,"given":"Jian","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic and Science Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanzhou","family":"Su","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic and Science Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic and Science Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoran","family":"Han","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic and Science Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yijie","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic and Science Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Peng, D., Zhang, Y., and Guan, H. 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