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Technology","award":["4072-115016"],"award-info":[{"award-number":["4072-115016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Building change detection is an important task in the remote sensing field, and the powerful feature extraction ability of the deep neural network model shows strong advantages in this task. However, the datasets used for this study are mostly three-band high-resolution remote sensing images from a single data source, and few spectral features limit the development of building change detection from multisource remote sensing images. To investigate the influence of spectral and texture features on the effect of building change detection based on deep learning, a multisource building change detection dataset (MS-HS BCD dataset) is produced in this paper using GF-1 high-resolution remote sensing images and Sentinel-2B multispectral remote sensing images. According to the different resolutions of each Sentinel-2B band, eight different multisource spectral data combinations are designed, and six advanced network models are selected for the experiments. After adding multisource spectral and texture feature data, the results show that the detection effects of the six networks improve to different degrees. Taking the MSF-Net network as an example, the F1-score and IOU improved by 0.67% and 1.09%, respectively, compared with high-resolution images, and by 7.57% and 6.21% compared with multispectral images.<\/jats:p>","DOI":"10.3390\/rs15092351","type":"journal-article","created":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T12:10:03Z","timestamp":1682943003000},"page":"2351","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Building Change Detection with Deep Learning by Fusing Spectral and Texture Features of Multisource Remote Sensing Images: A GF-1 and Sentinel 2B Data Case"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8987-7930","authenticated-orcid":false,"given":"Junfu","family":"Fan","sequence":"first","affiliation":[{"name":"School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China"},{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Mengzhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China"},{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Jiahao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China"},{"name":"College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"}]},{"given":"Jiwei","family":"Zuo","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China"}]},{"given":"Zongwen","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China"}]},{"given":"Min","family":"Ji","sequence":"additional","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266510, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xia, L., Chen, J., Luo, J., Zhang, J., Yang, D., and Shen, Z. 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