{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T05:40:13Z","timestamp":1727242813580},"reference-count":43,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T00:00:00Z","timestamp":1726876800000},"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":["42271478,2022YFC3004404,2023YFF1305303"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Accurate understanding of urban land use change information is of great significance for urban planning, urban monitoring, and disaster assessment. The use of Very-High-Resolution (VHR) remote sensing images for change detection on urban land features has gradually become mainstream. However, most existing transfer learning-based change detection models compute multiple deep image features, leading to feature redundancy. Therefore, we propose a Transfer Learning Change Detection Model Based on Change Feature Selection (TL-FS). The proposed method involves using a pretrained transfer learning model framework to compute deep features from multitemporal remote sensing images. A change feature selection algorithm is then designed to filter relevant change information. Subsequently, these change features are combined into a vector. The Change Vector Analysis (CVA) is employed to calculate the magnitude of change in the vector. Finally, the Fuzzy C-Means (FCM) classification is utilized to obtain binary change detection results. In this study, we selected four VHR optical image datasets from Beijing-2 for the experiment. Compared with the Change Vector Analysis and Spectral Gradient Difference, the TL-FS method had maximum increases of 26.41% in the F1-score, 38.04% in precision, 29.88% in recall, and 26.15% in the overall accuracy. The results of the ablation experiments also indicate that TL-FS could provide clearer texture and shape detections for dual-temporal VHR image changes. It can effectively detect complex features in urban scenes.<\/jats:p>","DOI":"10.3390\/rs16183507","type":"journal-article","created":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T13:15:07Z","timestamp":1727097307000},"page":"3507","source":"Crossref","is-referenced-by-count":0,"title":["Feature-Selection-Based Unsupervised Transfer Learning for Change Detection from VHR Optical Images"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-5720-2338","authenticated-orcid":false,"given":"Qiang","family":"Chen","sequence":"first","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"}]},{"given":"Peng","family":"Yue","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"}]},{"given":"Yingjun","family":"Xu","sequence":"additional","affiliation":[{"name":"Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China"},{"name":"School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9164-5805","authenticated-orcid":false,"given":"Shisong","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5473-8495","authenticated-orcid":false,"given":"Lei","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"}]},{"given":"Jianhui","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1080\/01431168908903939","article-title":"Review article digital change detection techniques using remotely-sensed data","volume":"10","author":"Singh","year":"1989","journal-title":"Int. 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