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However, due to the complex backgrounds of satellite video sequences and many rotation changes of highly dynamic targets, typical target tracking methods for natural scenes cannot be used directly for such tasks, and their robustness and accuracy are difficult to guarantee. To address these problems, an algorithm is proposed for remote sensing target tracking in satellite videos based on a variable\u2010angle\u2010adaptive Siamese network (VAASN). Specifically, the method is based on the fully convolutional Siamese network (Siamese\u2010FC). First, for the feature extraction stage, to reduce the impact of complex backgrounds, we present a new multifrequency feature representation method and introduce the octave convolution (OctConv) into the AlexNet architecture to adapt to the new feature representation. Then, for the tracking stage, to adapt to changes in target rotation, a variable\u2010angle\u2010adaptive module that uses a fast text detector with a single deep neural network (TextBoxes++) is introduced to extract angle information from the template frame and detection frames and performs angle consistency update operations on the detection frames. Finally, qualitative and quantitative experiments using satellite datasets show that the proposed method can improve tracking accuracy while achieving high efficiency.<\/jats:p>","DOI":"10.1049\/ipr2.12170","type":"journal-article","created":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T06:46:19Z","timestamp":1615963579000},"page":"1987-1997","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Remote sensing target tracking in satellite videos based on a variable\u2010angle\u2010adaptive Siamese network"],"prefix":"10.1049","volume":"15","author":[{"given":"Fukun","family":"Bi","sequence":"first","affiliation":[{"name":"Department of School of Information Science and Technology North China University of Technology  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2212-1351","authenticated-orcid":false,"given":"Jiayi","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of School of Information Science and Technology North China University of Technology  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhong","family":"Han","sequence":"additional","affiliation":[{"name":"Department of School of Information Science and Technology North China University of Technology  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanping","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of School of Information Science and Technology North China University of Technology  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingming","family":"Bian","sequence":"additional","affiliation":[{"name":"Qian Xuesen Laboratory Beijing Institute of Spacecraft System Engineering  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2021,3,17]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2017.2776899"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2916953"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2943366"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2019.2933488"},{"key":"e_1_2_6_6_1","doi-asserted-by":"crossref","unstructured":"Danelljan M. et\u00a0al.:Accurate scale estimation for robust visual tracking. 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