{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T13:05:34Z","timestamp":1742389534201,"version":"3.37.3"},"reference-count":49,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T00:00:00Z","timestamp":1607990400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Plan of China","award":["2016YFC0503004"]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41601180","41771203"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Bureau of Ecology and Environment of Shenzhen","award":["SZCG2018161498"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Classification and change analysis based on high spatial resolution imagery are highly desirable for urban landscapes. However, methods with both high accuracy and efficiency are lacking. Here, we present a novel approach that integrates backdating and transfer learning under an object-based framework. Backdating is used to optimize the target area to be classified, and transfer learning is used to select training samples for classification. We further compare the new approach with that of using backdating or transfer learning alone. We found: (1) The integrated new approach had higher overall accuracy for both classifications (85.33%) and change analysis (88.67%), which were 2.0% and 4.0% higher than that of backdating, and 9.3% and 9.0% higher than that of transfer learning, respectively. (2) Compared to approaches using backdating alone, the use of transfer learning in the new approach allows automatic sample selection for supervised classification, and thereby greatly improves the efficiency of classification, and also reduces the subjectiveness of sample selection. (3) Compared to approaches using transfer learning alone, the use of backdating in the new approach allows the classification focusing on the changed areas, only 16.4% of the entire study area, and therefore greatly improves the efficiency and largely avoid the false change. In addition, the use of a reference map for classification can improve accuracy. This new approach would be particularly useful for large area classification and change analysis.<\/jats:p>","DOI":"10.3390\/rs12244094","type":"journal-article","created":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T14:12:57Z","timestamp":1608041577000},"page":"4094","source":"Crossref","is-referenced-by-count":12,"title":["Integrating Backdating and Transfer Learning in an Object-Based Framework for High Resolution Image Classification and Change Analysis"],"prefix":"10.3390","volume":"12","author":[{"given":"Yuguo","family":"Qian","sequence":"first","affiliation":[{"name":"State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, No. 18 Shuangqing Road, Beijing 100085, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7323-4906","authenticated-orcid":false,"given":"Weiqi","family":"Zhou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, No. 18 Shuangqing Road, Beijing 100085, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China"}]},{"given":"Wenjuan","family":"Yu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, No. 18 Shuangqing Road, Beijing 100085, China"}]},{"given":"Lijian","family":"Han","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, No. 18 Shuangqing Road, Beijing 100085, China"}]},{"given":"Weifeng","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, No. 18 Shuangqing Road, Beijing 100085, China"}]},{"given":"Wenhui","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, No. 18 Shuangqing Road, Beijing 100085, China"},{"name":"Beijing Municipal Environmental Monitoring Center, No. 14 Chegongzhuang West Road, Beijing 100048, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"20666","DOI":"10.1073\/pnas.0704119104","article-title":"The emergence of land change science for global environmental change and sustainability","volume":"104","author":"Turner","year":"2007","journal-title":"Proc. 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