{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:19:23Z","timestamp":1760228363573,"version":"build-2065373602"},"reference-count":20,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T00:00:00Z","timestamp":1652227200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006245","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFC0408901","2018YFB2100500"],"award-info":[{"award-number":["2019YFC0408901","2018YFB2100500"]}],"id":[{"id":"10.13039\/501100006245","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFC0408901","2018YFB2100500"],"award-info":[{"award-number":["2019YFC0408901","2018YFB2100500"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"name":"\u201dSpatial monitoring of the environmental and ecological sustainable development in the Lancang-Mekong region\u201d project","award":["2019YFC0408901","2018YFB2100500"],"award-info":[{"award-number":["2019YFC0408901","2018YFB2100500"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The classification of optical satellite-derived remote sensing images is an important satellite remote sensing application. Due to the wide variety of artificial features and complex ground situations in urban areas, the classification of complex urban features has always been a focus of and challenge in the field of remote sensing image classification. Given the limited information that can be obtained from traditional optical satellite-derived remote sensing data of a classification area, it is difficult to classify artificial features in detail at the pixel level. With the development of technologies, such as satellite platforms and sensors, the data types acquired by remote sensing satellites have evolved from static images to dynamic videos. Compared with traditional satellite-derived images, satellite-derived videos contain increased ground object reflection information, especially information obtained from different observation angles, and can thus provide more information for classifying complex urban features and improving the corresponding classification accuracies. In this paper, first, we analyze urban-area, ground feature characteristics and satellite-derived video remote sensing data. Second, according to these characteristics, we design a pixel-level classification method based on the application of machine learning techniques to video remote sensing data that represents complex, urban-area ground features. Last, we conduct experiments on real data. The test results show that applying the method designed in this paper to classify dynamic, satellite-derived video remote sensing data can improve the classification accuracy of complex features in urban areas compared with the classification results obtained using static, satellite-derived remote sensing image data at the same resolution.<\/jats:p>","DOI":"10.3390\/rs14102324","type":"journal-article","created":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T23:08:36Z","timestamp":1652396916000},"page":"2324","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Method for Classifying Complex Features in Urban Areas Using Video Satellite Remote Sensing Data"],"prefix":"10.3390","volume":"14","author":[{"given":"Fanghong","family":"Ye","sequence":"first","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"},{"name":"Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of P.R. China, Beijing 100048, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6581-9872","authenticated-orcid":false,"given":"Tinghua","family":"Ai","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}]},{"given":"Jiaming","family":"Wang","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China"}]},{"given":"Yuan","family":"Yao","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China"}]},{"given":"Zheng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Ecology and Environment Monitoring and Scientific Research Center, Yangtze Basin Ecology and Environment Administration, Ministry of Ecology and Environment of the P.R. China, Wuhan 430014, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s11432-019-2784-4","article-title":"Deep feature extraction and motion representation for satellite video scene classification","volume":"63","author":"Gu","year":"2020","journal-title":"Sci. China Inf. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"14680","DOI":"10.3390\/rs71114680","article-title":"Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery","volume":"7","author":"Hu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7860","DOI":"10.1109\/TGRS.2020.2984656","article-title":"Detection of Event of Interest for Satellite Video Understanding","volume":"58","author":"Gu","year":"2020","journal-title":"IEEE Trans. Geosci. 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