{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:28:09Z","timestamp":1772555289745,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T00:00:00Z","timestamp":1679529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Fundamental Research Funds for the Central Universities","award":["JUSRP121072"],"award-info":[{"award-number":["JUSRP121072"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["BZ2020069"],"award-info":[{"award-number":["BZ2020069"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["21KJA520001"],"award-info":[{"award-number":["21KJA520001"]}]},{"name":"International Science and Technology Cooperation Project of Jiangsu Province","award":["JUSRP121072"],"award-info":[{"award-number":["JUSRP121072"]}]},{"name":"International Science and Technology Cooperation Project of Jiangsu Province","award":["BZ2020069"],"award-info":[{"award-number":["BZ2020069"]}]},{"name":"International Science and Technology Cooperation Project of Jiangsu Province","award":["21KJA520001"],"award-info":[{"award-number":["21KJA520001"]}]},{"name":"Major Program of University Natural Science Research of Jiangsu Province","award":["JUSRP121072"],"award-info":[{"award-number":["JUSRP121072"]}]},{"name":"Major Program of University Natural Science Research of Jiangsu Province","award":["BZ2020069"],"award-info":[{"award-number":["BZ2020069"]}]},{"name":"Major Program of University Natural Science Research of Jiangsu Province","award":["21KJA520001"],"award-info":[{"award-number":["21KJA520001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Object tracking using Hyperspectral Images (HSIs) obtains satisfactory result in distinguishing objects with similar colors. Yet, the tracking algorithm tends to fail when the target undergoes deformation. In this paper, a SiamRPN based hyperspectral tracker is proposed to deal with this problem. Firstly, a band selection method based on a genetic optimization method is designed for rapidly reducing the redundancy of information in HSIs. Specifically, three bands with highest joint entropy are selected. To solve the problem that the information of the template in the SiamRPN model decays over time, an update network is trained on the dataset from general objective tracking benchmark, which can obtain effective cumulative templates. The use of cumulative templates with spectral information makes it easier to track the deformed target. In addition, transfer learning of the pre-trained SiamRPN is designed to obtain a better model for HSIs. The experimental results show that the proposed tracker can obtain good tracking results over the entire public dataset, and that it is better than the other popular trackers when the target\u2019s deformation is qualitatively and quantitatively compared, achieving an overall success rate of 57.5% and a deformation challenge success rate of 70.8%.<\/jats:p>","DOI":"10.3390\/rs15071731","type":"journal-article","created":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T02:34:54Z","timestamp":1679625294000},"page":"1731","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["AD-SiamRPN: Anti-Deformation Object Tracking via an Improved Siamese Region Proposal Network on Hyperspectral Videos"],"prefix":"10.3390","volume":"15","author":[{"given":"Shiqing","family":"Wang","sequence":"first","affiliation":[{"name":"School of Artifical Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2702-9500","authenticated-orcid":false,"given":"Kun","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Artifical Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China"}]},{"given":"Jianlu","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Artifical Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China"}]},{"given":"Hongyu","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Automation, Wuxi University, Wuxi 214122, China"}]},{"given":"Peng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Artifical Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, S., Zhang, T., Cao, X., and Xu, C. 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