{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:57:31Z","timestamp":1760151451236,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,28]],"date-time":"2022-03-28T00:00:00Z","timestamp":1648425600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this study, an unsupervised infrared object-detection approach based on spatial\u2013temporal patch tensor and object selection is proposed to fully use effective temporal information and maintain a balance between object-detection performance and computation time. Initially, a spatial\u2013temporal patch tensor is proposed by performing median pooling function on patch tensors generated from consecutive frames to suppress sky or cloud clutter. Then, a contrast-boosted approach that incorporates morphological operations is proposed to improve the contrast between objects and background. Finally, an object-selection approach is proposed based on the cluster center derived from clustering locations and gray values, thereby decreasing the search scope of objects in the detection process. The experiments of five infrared sequence frames confirm that the proposed framework can obtain better results than most previous methods when handling heterogeneous scenes in terms of gray values. Experimental results of five real sequence frames also demonstrate that the spatial\u2013temporal patch tensor, the contrast-boosted approach, and object-selection approach can increase the recall ratio by 6.7, 2.21, and 1.14 percentage units and the precision ratio by 1.61, 3.44, and 11.79 percentage units, respectively. Moreover, the proposed framework can achieve an average F1 score of 0.9804 with about 1.85 s of computation time, demonstrating that it can obtain satisfactory object-detection performance with relatively low computation time.<\/jats:p>","DOI":"10.3390\/rs14071612","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T21:45:51Z","timestamp":1648590351000},"page":"1612","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Unsupervised Infrared Small-Object-Detection Approach of Spatial\u2013Temporal Patch Tensor and Object Selection"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5006-0840","authenticated-orcid":false,"given":"Ruixi","family":"Zhu","sequence":"first","affiliation":[{"name":"Department of Research, Nanjing Research Institute of Electronic Technology, Nanjing 210039, China"}]},{"given":"Long","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Department of Research, Nanjing Research Institute of Electronic Technology, Nanjing 210039, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3101","DOI":"10.1109\/JSTARS.2019.2920327","article-title":"Infrared small target detection based on derivative dissimilarity measure","volume":"12","author":"Cao","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"125459","DOI":"10.1109\/ACCESS.2020.3007481","article-title":"Thermal Object Detection in Difficult Weather Conditions Using YOLO","volume":"8","author":"Kristo","year":"2020","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5039","DOI":"10.1109\/JSTARS.2018.2877501","article-title":"Robust Infrared Small Target Detection Using Multiscale Gray and Variance Difference Measures","volume":"11","author":"Gao","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4150","DOI":"10.1109\/JSTARS.2021.3069032","article-title":"Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection","volume":"14","author":"Xue","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","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. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/JSTARS.2020.3038442","article-title":"Infrared Small Target Detection Utilizing the Enhanced Closest-Mean Background Estimation","volume":"14","author":"Han","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.infrared.2017.04.001","article-title":"Scale invariant SURF detector and automatic clustering segmentation for infrared small targets detection","volume":"83","author":"Zhang","year":"2017","journal-title":"Infrared Phys. Technol."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1016\/j.compeleceng.2018.05.009","article-title":"Infrared moving point target detection based on an anisotropic spatial-temporal fourth-order diffusion filter","volume":"68","author":"Hu","year":"2018","journal-title":"Comput. Electr. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2528","DOI":"10.1109\/JSTARS.2018.2828317","article-title":"Infrared Small Target Detection Based on Flux Density and Direction Diversity in Gradient Vector Field","volume":"11","author":"Liu","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","unstructured":"Mansoori, A.A.S.A., Swamidoss, I.N., Sayadi, S., and Almarzooqi, A. (2020, January 21\u201325). Analysis of different tracking algorithms applied on thermal infrared imagery for maritime surveillance systems. Proceedings of the Artificial Intelligence and Machine Learning in Defense Applications II, Online."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1109\/7.7174","article-title":"Optical moving target detection with 3-D matched filtering","volume":"24","author":"Reed","year":"2002","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_14","first-page":"303","article-title":"Detecting of small infrared moving object based on dynamic programming algorithm","volume":"33","author":"Huang","year":"2004","journal-title":"Infrared Laser Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"113109","DOI":"10.1117\/1.OE.53.11.113109","article-title":"Antivibration pipeline-filtering algorithm for maritime small target detection","volume":"53","author":"Wang","year":"2014","journal-title":"Opt. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Vaishnavi, R., Unnikrishnan, G., and Raj, A.A.B. (2019, January 17\u201318). Implementation of algorithms for Point target detection and tracking in Infrared image sequences. Proceedings of the 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), Bangalore, India.","DOI":"10.1109\/RTEICT46194.2019.9016871"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3737","DOI":"10.1109\/TGRS.2020.3022069","article-title":"Infrared Dim and Small Target Detection via Multiple Subspace Learning and Spatial-Temporal Patch-Tensor Model","volume":"59","author":"Sun","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"13403","DOI":"10.1007\/s11042-020-08616-z","article-title":"Efficient Object Detection and Classification of Heat Emitting Objects from Infrared Images Based on Deep Learning","volume":"79","author":"Algarni","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3677","DOI":"10.1109\/JSTARS.2019.2931566","article-title":"Infrared Small Target Detection Using Local and Nonlocal Spatial Information","volume":"12","author":"Li","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.infrared.2011.10.006","article-title":"Edge directional 2D LMS filter for infrared small target detection","volume":"55","author":"Bae","year":"2012","journal-title":"Infrared Phys. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1109\/31.1775","article-title":"The two-dimensional adaptive LMS (TDLMS) algorithm","volume":"35","author":"Hadhoud","year":"1988","journal-title":"IEEE Trans. Circuits Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.infrared.2005.04.006","article-title":"The design of Top-Hat morphological filter and application to infrared target detection","volume":"48","author":"Ming","year":"2006","journal-title":"Infrared Phys. Technol."},{"key":"ref_23","first-page":"74","article-title":"Max-mean and max-median filters for detection of small targets. Signal and Data Processing of Small Targets","volume":"3809","author":"Deshpande","year":"1999","journal-title":"Int. Soc. Opt. Photonics"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1049\/iet-ipr.2015.0744","article-title":"Infrared dim small target detection with high reliability using saliency map fusion","volume":"10","author":"Nasiri","year":"2016","journal-title":"IET Image Process."},{"key":"ref_25","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":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1007\/s10762-009-9518-2","article-title":"Small Target Detection Utilizing Robust Methods of the Human Visual System for IRST","volume":"30","author":"Kim","year":"2009","journal-title":"J. Infrared Millim. Terahertz Waves"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/TAES.2015.140878","article-title":"Infrared small-target detection using multi-scale gray difference weighted image entropy","volume":"52","author":"Deng","year":"2016","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1700","DOI":"10.1109\/LGRS.2017.2729512","article-title":"Infrared Small Target Detection via Nonnegativity-Constrained Variational Mode De-composition","volume":"10","author":"Wang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2428","DOI":"10.1109\/TPAMI.2015.2424870","article-title":"Finding the Secret of Image Saliency in the Frequency Domain","volume":"37","author":"Li","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1109\/LGRS.2016.2556218","article-title":"An Efficient Infrared Small Target Detection Method Based on Visual Contrast Mechanism","volume":"13","author":"Chen","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1049\/ipr2.12049","article-title":"Infrared small target detection based on non-convex triple tensor factorisation","volume":"15","author":"Rawat","year":"2020","journal-title":"IET Image Process."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","unstructured":"Mancera, L., and Portilla, J. (2006, January 8\u201311). L0-Norm-Based Sparse Representation through Alternate Projections. Proceedings of the International Conference on Image Processing, Atlanta, GA, USA.","DOI":"10.1109\/ICIP.2006.312819"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/0165-0114(91)90064-W","article-title":"L1-norm based fuzzy clustering","volume":"39","author":"Jajuga","year":"1991","journal-title":"Fuzzy Sets Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1111\/j.1365-2478.1990.tb01852.x","article-title":"Lp-NORM DECONVOLUTION1","volume":"38","author":"Debeye","year":"1990","journal-title":"Geophys. Prospect."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, L., Peng, L., Zhang, T., Cao, S., and Peng, Z. (2018). Infrared Small Target Detection via Non-Convex Rank Approximation Min-imization Joint l2,1 Norm. Remote Sens., 10.","DOI":"10.3390\/rs10111821"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.infrared.2014.10.022","article-title":"Small infrared target detection based on low-rank and sparse representation","volume":"68","author":"He","year":"2015","journal-title":"Infrared Phys. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.infrared.2017.01.009","article-title":"Non-negative infrared patch-image model: Robust target-background separation via partial sum minimization of singular values","volume":"81","author":"Dai","year":"2017","journal-title":"Infrared Phys. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1109\/34.85668","article-title":"Multidimensional orientation estimation with applications to texture analysis and optical flow","volume":"13","author":"Bigun","year":"1991","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1023\/A:1008009714131","article-title":"Coherence-enhancing diffusion filtering","volume":"31","author":"Weickert","year":"1999","journal-title":"Int. J. Comput. Vis."},{"key":"ref_41","unstructured":"Brown, M., Szeliski, R., and Winder, S. (2005, January 20\u201325). Multi-image matching using multi-scale oriented patches. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1109\/TPAMI.2019.2891760","article-title":"Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm","volume":"42","author":"Lu","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1090\/psapm\/040\/1059485","article-title":"The hadamard product","volume":"40","author":"Horn","year":"1990","journal-title":"Proc. Symp. Appl. Math."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"9128","DOI":"10.1364\/OE.27.009128","article-title":"Joint ptycho-tomography reconstruction through alternating direction method of multi-pliers","volume":"27","author":"Aslan","year":"2019","journal-title":"Opt. Express"},{"key":"ref_45","first-page":"2","article-title":"Morphology-based algorithm for point target detection in infrared backgrounds. Signal and Data Processing of Small Targets","volume":"1954","author":"Tom","year":"1993","journal-title":"Int. Soc. Opt. Photonics"},{"key":"ref_46","first-page":"149","article-title":"Realization of tophat transform and bothat transform of mathematical morphology","volume":"5","author":"Zhao","year":"2008","journal-title":"Inf. Technol."},{"key":"ref_47","unstructured":"(2022, March 06). A Dataset for Infrared Image Dim-Small Aircraft Target Detection and Tracking Underground\/air Background (V1). Available online: https:\/\/datapid.cn\/31253.11.sciencedb.902."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/j.patcog.2017.11.016","article-title":"Infrared small-dim target detection based on Markov random field guided noise modeling","volume":"76","author":"Gao","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_49","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":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1109\/LGRS.2019.2922347","article-title":"Infrared Small Target Detection Using Homogeneity-Weighted Local Contrast Measure","volume":"17","author":"Du","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_51","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_52","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_53","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016). Ssd: Single Shot Multibox Detector. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Mo, N., Yan, L., Zhu, R., and Xie, H. (2019). Class-Specific Anchor Based and Context-Guided Multi-Class Object Detection in High Resolution Remote Sensing Imagery with a Convolutional Neural Network. Remote Sens., 11.","DOI":"10.3390\/rs11030272"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/7\/1612\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:44:48Z","timestamp":1760136288000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/7\/1612"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,28]]},"references-count":55,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["rs14071612"],"URL":"https:\/\/doi.org\/10.3390\/rs14071612","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,3,28]]}}}