{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T15:31:59Z","timestamp":1776526319917,"version":"3.51.2"},"reference-count":63,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T00:00:00Z","timestamp":1710979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Program Project of Science and Technology Innovation of the Chinese Academy of Sciences","award":["KGFZD-135-20-03-02"],"award-info":[{"award-number":["KGFZD-135-20-03-02"]}]},{"name":"Key Program Project of Science and Technology Innovation of the Chinese Academy of Sciences","award":["CXJJ-23S016"],"award-info":[{"award-number":["CXJJ-23S016"]}]},{"name":"Innovation Foundation of Key Laboratory of Computational Optical Imaging Technology, CAS","award":["KGFZD-135-20-03-02"],"award-info":[{"award-number":["KGFZD-135-20-03-02"]}]},{"name":"Innovation Foundation of Key Laboratory of Computational Optical Imaging Technology, CAS","award":["CXJJ-23S016"],"award-info":[{"award-number":["CXJJ-23S016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Infrared small target detection plays a crucial role in both military and civilian systems. However, current detection methods face significant challenges in complex scenes, such as inaccurate background estimation, inability to distinguish targets from similar non-target points, and poor robustness across various scenes. To address these issues, this study presents a novel spatial\u2013temporal tensor model for infrared small target detection. In our method, we introduce the tensor tree rank to capture global structure in a more balanced strategy, which helps achieve more accurate background estimation. Meanwhile, we design a novel self-adaptive local prior weight by evaluating the level of clutter and noise content in the image. It mitigates the imbalance between target enhancement and background suppression. Then, the spatial\u2013temporal total variation (STTV) is used as a joint regularization term to help better remove noise and obtain better detection performance. Finally, the proposed model is efficiently solved by the alternating direction multiplier method (ADMM). Extensive experiments demonstrate that our method achieves superior detection performance when compared with other state-of-the-art methods in terms of target enhancement, background suppression, and robustness across various complex scenes. Furthermore, we conduct an ablation study to validate the effectiveness of each module in the proposed model.<\/jats:p>","DOI":"10.3390\/rs16061108","type":"journal-article","created":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T11:37:22Z","timestamp":1711021042000},"page":"1108","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Infrared Small Target Detection Based on Tensor Tree Decomposition and Self-Adaptive Local Prior"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0807-285X","authenticated-orcid":false,"given":"Guiyu","family":"Zhang","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Department of Key Laboratory of Computational Optical Imagine Technology, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Zhenyu","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Qunbo","family":"Lv","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Department of Key Laboratory of Computational Optical Imagine Technology, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Baoyu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Department of Key Laboratory of Computational Optical Imagine Technology, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Wenjian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Department of Key Laboratory of Computational Optical Imagine Technology, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Jiaao","family":"Li","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Department of Key Laboratory of Computational Optical Imagine Technology, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Zheng","family":"Tan","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Department of Key Laboratory of Computational Optical Imagine Technology, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2496","DOI":"10.1016\/j.procs.2020.03.302","article-title":"Review on recent development in infrared small target detection algorithms","volume":"167","author":"Rawat","year":"2020","journal-title":"Procedia Comput. 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