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In this paper, a novel algorithm is proposed to address the challenges prevalent in the existing hyperspectral video target tracking approaches. The proposed approach employs deep features along with spectral matching reduction and adaptive-scale 3D hog features to track the objects even when the scale is varying. Spectral matching reduction is adopted to estimate the spectral curve of the selected target region using a weighted combination of the global and local spectral curves. In addition to the deep features, adaptive-scale 3D hog features are extracted using cube-level features at three different scales. The four weak response maps thus obtained are then combined using adaptive weights to yield a strong response map. Finally, the region proposal module is utilized to estimate the target box. The proposed strategies make the approach robust against scale variations of the target. A comparative study on different hyperspectral video sequences illustrate the superior performance of the proposed algorithm as compared to the state-of-the-art approaches.<\/jats:p>","DOI":"10.3390\/rs14235958","type":"journal-article","created":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T03:00:13Z","timestamp":1669345213000},"page":"5958","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Hyperspectral Video Target Tracking Based on Deep Features with Spectral Matching Reduction and Adaptive Scale 3D Hog Features"],"prefix":"10.3390","volume":"14","author":[{"given":"Zhe","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Physics, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Xuguang","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Wuxi University, Wuxi 214105, China"},{"name":"School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2439-6815","authenticated-orcid":false,"given":"Dong","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Physics, Xidian University, Xi\u2019an 710071, China"},{"name":"School of Electronics and Information Engineering, Wuxi University, Wuxi 214105, China"},{"name":"School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Pattathal V.","family":"Arun","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Group, Indian Institute of Information Technology, Sri City 441108, India"}]},{"given":"Huixin","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Physics, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2702-9500","authenticated-orcid":false,"given":"Kun","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer, Jiangnan University, Wuxi 214122, China"}]},{"given":"Jianling","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Wuxi University, Wuxi 214105, China"},{"name":"School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,24]]},"reference":[{"key":"ref_1","first-page":"103","article-title":"Target tracking from infrared imagery via an improved appearance model","volume":"104","author":"Zhao","year":"2019","journal-title":"Infrared Phys. 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