{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:39:56Z","timestamp":1764175196504,"version":"build-2065373602"},"reference-count":72,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T00:00:00Z","timestamp":1649203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Chang Jiang Scholars Program; the Civil Aviation Program; the Space based on orbit real-time processing technology; the National Science Foundation for Young Scientists of China; National Natural Science Foundation of China","award":["grant T2012122; grant B0201; grant 2018-JCJQ-ZQ-046; Grant 62101046; No. 62136001"],"award-info":[{"award-number":["grant T2012122; grant B0201; grant 2018-JCJQ-ZQ-046; Grant 62101046; No. 62136001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Building extraction using very high resolution (VHR) optical remote sensing imagery is an essential interpretation task that impacts human life. However, buildings in different environments exhibit various scales, complicated spatial distributions, and different imaging conditions. Additionally, with the spatial resolution of images increasing, there are diverse interior details and redundant context information present in building and background areas. Thus, the above-mentioned situations would create large intra-class variances and poor inter-class discrimination, leading to uncertain feature descriptions for building extraction, which would result in over- or under-extraction phenomena. In this article, a novel hierarchical disentangling network with an encoder\u2013decoder architecture called HDNet is proposed to consider both the stable and uncertain feature description in a convolution neural network (CNN). Next, a hierarchical disentangling strategy is set up to individually generate strong and weak semantic zones using a newly designed feature disentangling module (FDM). Here, the strong and weak semantic zones set up the stable and uncertain description individually to determine a more stable semantic main body and uncertain semantic boundary of buildings. Next, a dual-stream semantic feature description is built to gradually integrate strong and weak semantic zones by the designed component feature fusion module (CFFM), which is able to generate a powerful semantic description for more complete and refined building extraction. Finally, extensive experiments are carried out on three published datasets (i.e., WHU satellite, WHU aerial, and INRIA), and the comparison results show that the proposed HDNet outperforms other state-of-the-art (SOTA) methods.<\/jats:p>","DOI":"10.3390\/rs14071767","type":"journal-article","created":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T21:08:22Z","timestamp":1649365702000},"page":"1767","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Hierarchical Disentangling Network for Building Extraction from Very High Resolution Optical Remote Sensing Imagery"],"prefix":"10.3390","volume":"14","author":[{"given":"Jianhao","family":"Li","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Embedded Real-Time Information Processing Technology, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Yin","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Embedded Real-Time Information Processing Technology, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Shan","family":"Dong","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Embedded Real-Time Information Processing Technology, Beijing Institute of Technology, Beijing 100081, China"},{"name":"State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China"}]},{"given":"Peng","family":"Gao","sequence":"additional","affiliation":[{"name":"Shanghai AI Laboratory, Shanghai 200232, China"}]},{"given":"Hao","family":"Dong","sequence":"additional","affiliation":[{"name":"Center on Frontiers of Computing Studies and School of Electronic Engineering and Computer Science, Peking University, Beijing 100087, China"}]},{"given":"He","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Embedded Real-Time Information Processing Technology, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Liang","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Embedded Real-Time Information Processing Technology, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Lianlin","family":"Li","sequence":"additional","affiliation":[{"name":"Center on Frontiers of Computing Studies and School of Electronic Engineering and Computer Science, Peking University, Beijing 100087, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101577","DOI":"10.1016\/j.ijdrr.2020.101577","article-title":"Scenario-Based Seismic Vulnerability and Hazard Analyses to Help Direct Disaster Risk Reduction in Rural Weinan, China","volume":"48","author":"Liu","year":"2020","journal-title":"Int. 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