{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T16:39:04Z","timestamp":1778344744890,"version":"3.51.4"},"reference-count":68,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,14]],"date-time":"2021-05-14T00:00:00Z","timestamp":1620950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61771391"],"award-info":[{"award-number":["61771391"]}]},{"name":"Key R &amp; D plan of Shaanxi Province","award":["2020ZDLGY07-11"],"award-info":[{"award-number":["2020ZDLGY07-11"]}]},{"name":"Science, Technology and Innovation Commission of Shenzhen Municipality","award":["JCYJ20170815162956949, CYJ20180306171146740"],"award-info":[{"award-number":["JCYJ20170815162956949, CYJ20180306171146740"]}]},{"name":"Natural Science basic Research Plan in Shaanxi Province of China","award":["2018JM6056"],"award-info":[{"award-number":["2018JM6056"]}]},{"name":"the faculty research fund of Sejong University in 2021","award":["Sejong-2021"],"award-info":[{"award-number":["Sejong-2021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper presents a correlation filter object tracker based on fast spatial-spectral features (FSSF) to realize robust, real-time object tracking in hyperspectral surveillance video. Traditional object tracking in surveillance video based only on appearance information often fails in the presence of background clutter, low resolution, and appearance changes. Hyperspectral imaging uses unique spectral properties as well as spatial information to improve tracking accuracy in such challenging environments. However, the high-dimensionality of hyperspectral images causes high computational costs and difficulties for discriminative feature extraction. In FSSF, the real-time spatial-spectral convolution (RSSC) kernel is updated in real time in the Fourier transform domain without offline training to quickly extract discriminative spatial-spectral features. The spatial-spectral features are integrated into correlation filters to complete the hyperspectral tracking. To validate the proposed scheme, we collected a hyperspectral surveillance video (HSSV) dataset consisting of 70 sequences in 25 bands. Extensive experiments confirm the advantages and the efficiency of the proposed FSSF for object tracking in hyperspectral video tracking in challenging conditions of background clutter, low resolution, and appearance changes.<\/jats:p>","DOI":"10.3390\/rs13101922","type":"journal-article","created":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T02:31:34Z","timestamp":1621218694000},"page":"1922","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Object Tracking in Hyperspectral-Oriented Video with Fast Spatial-Spectral Features"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3106-4301","authenticated-orcid":false,"given":"Lulu","family":"Chen","sequence":"first","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University, Xi\u2019an 710129, China"},{"name":"Research &amp; Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518057, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6974-7327","authenticated-orcid":false,"given":"Yongqiang","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University, Xi\u2019an 710129, China"},{"name":"Research &amp; Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518057, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaxin","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University, Xi\u2019an 710129, China"},{"name":"Research &amp; Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518057, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaxin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University, Xi\u2019an 710129, China"},{"name":"Research &amp; Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518057, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University, Xi\u2019an 710129, China"},{"name":"Research &amp; Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518057, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3741-1124","authenticated-orcid":false,"given":"Jonathan Cheung-Wai","family":"Chan","sequence":"additional","affiliation":[{"name":"Department of Electronics and Informatics, Vrije Universiteit Brussel, Brussels 1050, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0335-6526","authenticated-orcid":false,"given":"Seong G.","family":"Kong","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Sejong University, Seoul 05006, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1109\/TCSVT.2019.2897980","article-title":"Saliency-Aware Convolution Neural Network for Ship Detection in Surveillance Video","volume":"30","author":"Shao","year":"2019","journal-title":"IEEE Trans. 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