{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T22:57:03Z","timestamp":1772319423675,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T00:00:00Z","timestamp":1715212800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Infrared (IR) imaging-based detection systems are of vital significance in the domains of early warning and security, necessitating a high level of precision and efficiency in infrared small moving target detection. IR targets often appear dim and small relative to the background and are easily buried by noise and difficult to detect. A novel high-speed spatial\u2013temporal saliency model (HS-STSM) based on a guided filter (GF) is proposed, which innovatively introduces GF into IR target detection to extract the local anisotropy saliency in the spatial domain, and substantially suppresses the background region as well as the bright clutter false alarms present in the background. Moreover, the proposed model extracts the motion saliency of the target in the temporal domain through vectorization of IR image sequences. Additionally, the proposed model significantly improves the detection efficiency through a vectorized filtering process and effectively suppresses edge components in the background by integrating a prior weight. Experiments conducted on five real infrared image sequences demonstrate the superior performance of the model compared to existing algorithms in terms of the detection rate, noise suppression, real-time processing, and robustness to the background.<\/jats:p>","DOI":"10.3390\/rs16101685","type":"journal-article","created":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T10:31:16Z","timestamp":1715250676000},"page":"1685","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["High-Speed Spatial\u2013Temporal Saliency Model: A Novel Detection Method for Infrared Small Moving Targets Based on a Vectorized Guided Filter"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-9816-3146","authenticated-orcid":false,"given":"Aersi","family":"Aliha","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4328-6427","authenticated-orcid":false,"given":"Yuhan","family":"Liu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"}]},{"given":"Guangyao","family":"Zhou","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"}]},{"given":"Yuxin","family":"Hu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Eysa, R., and Hamdulla, A. (2019, January 10\u201311). Issues on Infrared Dim Small Target Detection and Tracking. Proceedings of the 2019 International Conference on Smart Grid and Electrical Automation (ICSGEA), Xiangtan, China.","DOI":"10.1109\/ICSGEA.2019.00108"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9279","DOI":"10.1364\/AO.57.009279","article-title":"Improved small moving target detection method in infrared sequences under a rotational background","volume":"57","author":"Tong","year":"2018","journal-title":"Appl. Opt."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tom, V.T., Peli, T., Leung, M., and Bondaryk, J.E. (1993, January 22). Morphology-based algorithm for point target detection in infrared backgrounds. Proceedings of the Defense, Security, and Sensing, Orlando, FL, USA.","DOI":"10.1117\/12.157758"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Deshpande, S.D., Er, M.H., Venkateswarlu, R., and Chan, P. (1999, January 4). Max-mean and max-median filters for detection of small targets. Proceedings of the Optics and Photonics, Denver, CO, USA.","DOI":"10.1117\/12.364049"},{"key":"ref_5","unstructured":"Tomasi, C., and Manduchi, R. (1998, January 7). Bilateral filtering for gray and color images. Proceedings of the Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), Bombay, India."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4996","DOI":"10.1109\/TIP.2013.2281420","article-title":"Infrared Patch-Image Model for Small Target Detection in a Single Image","volume":"22","author":"Gao","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3752","DOI":"10.1109\/JSTARS.2017.2700023","article-title":"Reweighted Infrared Patch-Tensor Model With Both Nonlocal and Local Priors for Single-Frame Small Target Detection","volume":"10","author":"Dai","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, L., and Peng, Z. (2019). Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm. Remote Sens., 11.","DOI":"10.3390\/rs11040382"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hu, Y., Ma, Y., Pan, Z., and Liu, Y. (2022). Infrared Dim and Small Target Detection from Complex Scenes via Multi-Frame Spatial-Temporal Patch-Tensor Model. Remote Sens., 14.","DOI":"10.3390\/rs14092234"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/TGRS.2013.2242477","article-title":"A Local Contrast Method for Small Infrared Target Detection","volume":"52","author":"Chen","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.patcog.2016.04.002","article-title":"Multiscale patch-based contrast measure for small infrared target detection","volume":"58","author":"Wei","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/LGRS.2017.2772030","article-title":"High-Boost-Based Multiscale Local Contrast Measure for Infrared Small Target Detection","volume":"15","author":"Shi","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1822","DOI":"10.1109\/LGRS.2019.2954578","article-title":"A Local Contrast Method for Infrared Small-Target Detection Utilizing a Tri-Layer Window","volume":"17","author":"Han","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7505705","DOI":"10.1109\/LGRS.2021.3133649","article-title":"Infrared Small Target Detection Based on Weighted Three-Layer Window Local Contrast","volume":"19","author":"Cui","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.infrared.2016.02.010","article-title":"Infrared moving point target detection based on spatial\u2013temporal local contrast filter","volume":"76","author":"Deng","year":"2016","journal-title":"Infrared Phys. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.1109\/LGRS.2019.2954715","article-title":"Infrared Moving Small-Target Detection Using Spatial\u2013Temporal Local Difference Measure","volume":"17","author":"Du","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ma, Y., Liu, Y., Pan, Z., and Hu, Y. (2023). Method of Infrared Small Moving Target Detection Based on Coarse-to-Fine Structure in Complex Scenes. Remote Sens., 15.","DOI":"10.3390\/rs15061508"},{"key":"ref_18","first-page":"1046250","article-title":"Small target detection in infrared image using convolutional neural networks","volume":"Volume 10462","author":"Jiang","year":"2017","journal-title":"Proceedings of the AOPC 2017: Optical Sensing and Imaging Technology and Applications"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_20","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative Adversarial Nets. Proceedings of the 27th International Conference on Neural Information Processing Systems\u2014Volume 2, NIPS\u201914, Montreal, QC, Canada."},{"key":"ref_21","first-page":"7000405","article-title":"APAFNet: Single-Frame Infrared Small Target Detection by Asymmetric Patch Attention Fusion","volume":"20","author":"Wang","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1109\/TPAMI.2012.213","article-title":"Guided Image Filtering","volume":"35","author":"He","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","unstructured":"He, K., and Sun, J. (2015). Fast Guided Filter. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1049\/el:20081781","article-title":"Generalised-structure-tensor-based infrared small target detection","volume":"44","author":"Gao","year":"2008","journal-title":"Electron. Lett."},{"key":"ref_25","first-page":"12","article-title":"A dataset for infrared detection and tracking of dim-small aircraft targets under ground\/air background","volume":"5","author":"Hui","year":"2020","journal-title":"China Sci. 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