{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:22:03Z","timestamp":1760239323425,"version":"build-2065373602"},"reference-count":64,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,3]],"date-time":"2020-11-03T00:00:00Z","timestamp":1604361600000},"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":["41701504,41971400"],"award-info":[{"award-number":["41701504,41971400"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The emergence of very high resolution (VHR) images contributes to big challenges in change detection. It is hard for traditional pixel-level approaches to achieve satisfying performance due to radiometric difference. This work proposes a novel feature descriptor that is based on spectrum-trend and shape context for VHR remote sensing images. The proposed method is mainly composed of two aspects. The spectrum-trend graph is generated first, and then the shape context is applied in order to describe the shape of spectrum-trend. By constructing spectrum-trend graph, spatial and spectral information is integrated effectively. The approach is performed and assessed by QuickBird and SPOT-5 satellite images. The quantitative analysis of comparative experiments proves the effectiveness of the proposed technique in dealing with the radiometric difference and improving the accuracy of change detection. The results indicate that the overall accuracy and robustness are both boosted. Moreover, this work provides a novel viewpoint for discriminating changed and unchanged pixels by comparing the shape similarity of local spectrum-trend.<\/jats:p>","DOI":"10.3390\/rs12213606","type":"journal-article","created":{"date-parts":[[2020,11,3]],"date-time":"2020-11-03T09:09:32Z","timestamp":1604394572000},"page":"3606","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Unsupervised Change Detection Using Spectrum-Trend and Shape Similarity Measure"],"prefix":"10.3390","volume":"12","author":[{"given":"Yi","family":"Tian","sequence":"first","affiliation":[{"name":"MNR Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Ming","family":"Hao","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Hua","family":"Zhang","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1109\/JPROC.2012.2197169","article-title":"A novel framework for the design of change-detection systems for very-high-resolution remote sensing images","volume":"101","author":"Bruzzone","year":"2013","journal-title":"Proc. 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