{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T01:25:40Z","timestamp":1781659540308,"version":"3.54.5"},"reference-count":49,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,5]],"date-time":"2019-12-05T00:00:00Z","timestamp":1575504000000},"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":["41601405"],"award-info":[{"award-number":["41601405"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Major Projects of the National Natural Science Foundation of China","award":["41590845"],"award-info":[{"award-number":["41590845"]}]},{"DOI":"10.13039\/501100012246","name":"Priority Academic Program Development of Jiangsu Higher Education Institutions","doi-asserted-by":"publisher","award":["none"],"award-info":[{"award-number":["none"]}],"id":[{"id":"10.13039\/501100012246","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011131","name":"State Key Laboratory of Resources and Environmental Information System","doi-asserted-by":"publisher","award":["none"],"award-info":[{"award-number":["none"]}],"id":[{"id":"10.13039\/501100011131","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangsu Provincial Land and Resources Science and Technology Project","award":["2018054"],"award-info":[{"award-number":["2018054"]}]},{"name":"Xuzhou Land and Resources Bureau Science and Technology Project","award":["XZGTKJ2018001"],"award-info":[{"award-number":["XZGTKJ2018001"]}]},{"name":"Xuzhou Science and Technology Key R &amp; D Program (Social Development) Project","award":["KC18139"],"award-info":[{"award-number":["KC18139"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate extraction of buildings using high spatial resolution imagery is essential to a wide range of urban applications. However, it is difficult to extract semantic features from a variety of complex scenes (e.g., suburban, urban and urban village areas) because various complex man-made objects usually appear heterogeneous with large intra-class and low inter-class variations. The automatic extraction of buildings is thus extremely challenging. The fully convolutional neural networks (FCNs) developed in recent years have performed well in the extraction of urban man-made objects due to their ability to learn state-of-the-art features and to label pixels end-to-end. One of the most successful FCNs used in building extraction is U-net. However, the commonly used skip connection and feature fusion refinement modules in U-net often ignore the problem of feature selection, and the ability to extract smaller buildings and refine building boundaries needs to be improved. In this paper, we propose a trainable chain fully convolutional neural network (CFCN), which fuses high spatial resolution unmanned aerial vehicle (UAV) images and the digital surface model (DSM) for building extraction. Multilevel features are obtained from the fusion data, and an improved U-net is used for the coarse extraction of the building. To solve the problem of incomplete extraction of building boundaries, a U-net network is introduced by chain, which is used for the introduction of a coarse building boundary constraint, hole filling, and \"speckle\" removal. Typical areas such as suburban, urban, and urban villages were selected for building extraction experiments. The results show that the CFCN achieved recall of 98.67%, 98.62%, and 99.52% and intersection over union (IoU) of 96.23%, 96.43%, and 95.76% in suburban, urban, and urban village areas, respectively. Considering the IoU in conjunction with the CFCN and U-net resulted in improvements of 6.61%, 5.31%, and 6.45% in suburban, urban, and urban village areas, respectively. The proposed method can extract buildings with higher accuracy and with clearer and more complete boundaries.<\/jats:p>","DOI":"10.3390\/rs11242912","type":"journal-article","created":{"date-parts":[[2019,12,5]],"date-time":"2019-12-05T11:16:23Z","timestamp":1575544583000},"page":"2912","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Accurate Building Extraction from Fused DSM and UAV Images Using a Chain Fully Convolutional Neural Network"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8808-7961","authenticated-orcid":false,"given":"Wei","family":"Liu","sequence":"first","affiliation":[{"name":"School of Geography, Geomatics and Planning, Jiangsu Normal University, Xu Zhou 221116, China"},{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"MengYuan","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Geography, Geomatics and Planning, Jiangsu Normal University, Xu Zhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Geography, Geomatics and Planning, Jiangsu Normal University, Xu Zhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zihui","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Geography, Geomatics and Planning, Jiangsu Normal University, Xu Zhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"ErZhu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geography, Geomatics and Planning, Jiangsu Normal University, Xu Zhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianpeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geography, Geomatics and Planning, Jiangsu Normal University, Xu Zhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5311-8761","authenticated-orcid":false,"given":"Tao","family":"Pei","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang Design Institute of Water Conservancy &amp; Hydro-electric Power, Hangzhou 310002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1007\/s12517-016-2664-7","article-title":"Automatic building extraction from very high-resolution image and LiDAR data with SVM algorithm","volume":"9","author":"Karsli","year":"2016","journal-title":"Arab. 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