{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:47:24Z","timestamp":1775069244423,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T00:00:00Z","timestamp":1634515200000},"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":["41971311,42101381,41901282"],"award-info":[{"award-number":["41971311,42101381,41901282"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the national natural science foundation of Anhui","award":["2008085QD188"],"award-info":[{"award-number":["2008085QD188"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Building change detection has always been an important research focus in production and urbanization. In recent years, deep learning methods have demonstrated a powerful ability in the field of detecting remote sensing changes. However, due to the heterogeneity of remote sensing and the characteristics of buildings, the current methods do not present an effective means to perceive building changes or the ability to fuse multi-temporal remote sensing features, which leads to fragmented and incomplete results. In this article, we propose a multi-branched network structure to fuse the semantic information of the building changes at different levels. In this model, two accessory branches were used to guide the buildings\u2019 semantic information under different time sequences, and the main branches can merge the change information. In addition, we also designed a feature enhancement layer to further strengthen the integration of the main and accessory branch information. For ablation experiments, we designed experiments on the above optimization process. For MDEFNET, we designed experiments which compare with typical deep learning model and recent deep learning change detection methods. Experimentation with the WHU Building Change Detection Dataset showed that the method in this paper obtained accuracies of 0.8526, 0.9418, and 0.9204 in Intersection over Union (IoU), Recall, and F1 Score, respectively, which could assess building change areas with complete boundaries and accurate results.<\/jats:p>","DOI":"10.3390\/rs13204171","type":"journal-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T21:31:26Z","timestamp":1634765486000},"page":"4171","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Multi-Feature Enhanced Building Change Detection Based on Semantic Information Guidance"],"prefix":"10.3390","volume":"13","author":[{"given":"Junkang","family":"Xue","sequence":"first","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"}]},{"given":"Hao","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Spacecraft System Engineering, Beijing 100094, China"}]},{"given":"Hui","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"},{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei, Hefei 230601, China"},{"name":"Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3594-7953","authenticated-orcid":false,"given":"Biao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"},{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1983-5978","authenticated-orcid":false,"given":"Penghai","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"},{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3967-6481","authenticated-orcid":false,"given":"Jaewan","family":"Choi","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Chungbuk National University, Chungju 28644, Korea"}]},{"given":"Lixiao","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Design Group, Shandong Jianzhu University, Jinan 250101, China"}]},{"given":"Yanlan","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"},{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei, Hefei 230601, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111374","DOI":"10.1016\/j.rse.2019.111374","article-title":"Assessing spatial-temporal dynamics of urban expansion, vegetation greenness and photosynthesis in megacity Shanghai, China during 2000\u20132016","volume":"233","author":"Zhong","year":"2019","journal-title":"Remote Sens. 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