{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T05:28:57Z","timestamp":1767850137154,"version":"3.49.0"},"reference-count":44,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,7,2]],"date-time":"2018-07-02T00:00:00Z","timestamp":1530489600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61675099"],"award-info":[{"award-number":["61675099"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61701233"],"award-info":[{"award-number":["61701233"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Infrared image segmentation plays a significant role in many burgeoning applications of remote sensing, such as environmental monitoring, traffic surveillance, air navigation and so on. However, the precision is limited due to the blurred edge, low contrast and intensity inhomogeneity caused by infrared imaging. To overcome these challenges, a level set method using global and local information is proposed in this paper. In our method, a hybrid signed pressure function is constructed by fusing a global term and a local term adaptively. The global term is represented by the global average intensity, which effectively accelerates the evolution when the evolving curve is far away from the object. The local term is represented by a multi-feature-based signed driving force, which accurately guides the curve to approach the real boundary when it is near the object. Then, the two terms are integrated via an adaptive weight matrix calculated based on the range value of each pixel. Under the framework of geodesic active contour model, a new level set formula is obtained by substituting the proposed signed pressure function for the edge stopping function. In addition, a Gaussian convolution is applied to regularize the level set function for the purpose of avoiding the computationally expensive re-initialization. By iteration, the object of interest can be segmented when the level set function converges. Both qualitative and quantitative experiments verify that our method outperforms other state-of-the-art level set methods in terms of accuracy and robustness with the initial contour being set randomly.<\/jats:p>","DOI":"10.3390\/rs10071039","type":"journal-article","created":{"date-parts":[[2018,7,2]],"date-time":"2018-07-02T10:56:52Z","timestamp":1530529012000},"page":"1039","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A Level Set Method for Infrared Image Segmentation Using Global and Local Information"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4983-3180","authenticated-orcid":false,"given":"Minjie","family":"Wan","sequence":"first","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"},{"name":"Department of Electrical and Computer Engineering, Computer Vision and Systems Laboratory, Laval University, 1065 av. de la M\u00e9decine, Quebec City, QC G1V 0A6, Canada"},{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guohua","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhong","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weixian","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kan","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"},{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8777-2008","authenticated-orcid":false,"given":"Xavier","family":"Maldague","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Computer Vision and Systems Laboratory, Laval University, 1065 av. de la M\u00e9decine, Quebec City, QC G1V 0A6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9686","DOI":"10.1364\/AO.56.009686","article-title":"Infrared image enhancement algorithm based on adaptive histogram segmentation","volume":"56","author":"Huang","year":"2017","journal-title":"Appl. Opt."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zingoni, A., Diani, M., and Corsini, G. (2017). A Flexible Algorithm for Detecting Challenging Moving Objects in Real-Time within IR Video Sequences. Remote Sens., 9.","DOI":"10.3390\/rs9111128"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.infrared.2018.03.012","article-title":"Sea-land segmentation for infrared remote sensing images based on superpixels and multi-scale features","volume":"91","author":"Lei","year":"2018","journal-title":"Infrared Phys. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.infrared.2018.04.003","article-title":"Particle swarm optimization-based local entropy weighted histogram equalization for infrared image enhancement","volume":"91","author":"Wan","year":"2018","journal-title":"Infrared Phys. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1016\/0031-3203(93)90135-J","article-title":"A review on image segmentation techniques","volume":"26","author":"Pal","year":"1993","journal-title":"Pattern Recognit."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lee, L.K., Liew, S.C., and Thong, W.J. (2015). A review of image segmentation methodologies in medical image. Advanced Computer and Communication Engineering Technology, Springer.","DOI":"10.1007\/978-3-319-07674-4_99"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.patcog.2016.07.022","article-title":"Robust noise region-based active contour model via local similarity factor for image segmentation","volume":"61","author":"Niu","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2399","DOI":"10.1080\/09500340.2017.1366564","article-title":"A novel level set method for image segmentation by combining local and global information","volume":"64","author":"Cao","year":"2017","journal-title":"J. Mod. Opt."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1023\/A:1007979827043","article-title":"Geodesic active contours","volume":"22","author":"Caselles","year":"1997","journal-title":"Int. J. Comput. Vis."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s00138-011-0363-7","article-title":"Active contour model combining region and edge information","volume":"24","author":"Tian","year":"2013","journal-title":"Mach. Vis. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1109\/TPAMI.2007.70713","article-title":"Finsler active contours","volume":"30","author":"Melonakos","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","unstructured":"Paragios, N. (2004, January 15\u201318). Variational methods and partial differential equations in cardiac image analysis. Proceedings of the IEEE International Symposium on Biomedical Imaging: Nano to Macro, Arlington, VA, USA."},{"key":"ref_13","first-page":"071001-071001","article-title":"Fast hybrid fitting energy-based active contour model for target detection","volume":"9","author":"Wang","year":"2011","journal-title":"Chin. Opt. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1016\/j.ijleo.2018.01.004","article-title":"Active contour model based on local and global Gaussian fitting energy for medical image segmentation","volume":"158","author":"Zhao","year":"2018","journal-title":"Opt. Int. J. Light Electron. Opt."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/83.902291","article-title":"Active contours without edges","volume":"10","author":"Chan","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1002\/cpa.3160420503","article-title":"Optimal approximations by piecewise smooth functions and associated variational problems","volume":"42","author":"Mumford","year":"1989","journal-title":"Commun. Pure Appl. Math."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2237","DOI":"10.1364\/JOSAA.32.002237","article-title":"Region-based active contours with cosine fitting energy for image segmentation","volume":"32","author":"Wang","year":"2015","journal-title":"J. Opt. Soc. Am. A Opt. Image Sci. Vis."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1109\/83.935033","article-title":"Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification","volume":"10","author":"Tsai","year":"2001","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1023\/A:1020874308076","article-title":"A multiphase level set framework for image segmentation using the Mumford and Shah model","volume":"50","author":"Vese","year":"2002","journal-title":"Int. J. Comput. Vis."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1016\/j.imavis.2009.10.009","article-title":"Active contours with selective local or global segmentation: A new formulation and level set method","volume":"28","author":"Zhang","year":"2010","journal-title":"Image Vis. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1940","DOI":"10.1109\/TIP.2008.2002304","article-title":"Minimization of region-scalable fitting energy for image segmentation","volume":"17","author":"Li","year":"2008","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","first-page":"281","article-title":"Active contours based on image Laplacian fitting energy","volume":"18","author":"Zhang","year":"2009","journal-title":"Chin. J. Electron."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.1016\/j.patcog.2013.11.014","article-title":"Robust level set image segmentation via a local correntropy-based K-means clustering","volume":"47","author":"Wang","year":"2014","journal-title":"Pattern Recognit."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1016\/j.compmedimag.2009.04.010","article-title":"Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation","volume":"33","author":"Wang","year":"2009","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1016\/j.imavis.2013.08.003","article-title":"A new level set method for inhomogeneous image segmentation","volume":"31","author":"Dong","year":"2013","journal-title":"Image Vis. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Nie, X., Duan, Y., Huang, Y., and Luo, S. (2010, January 7). A benchmark for interactive image segmentation algorithms. Proceedings of the IEEE Workshop on Person-Oriented Vision (POV), Kona, HI, USA.","DOI":"10.1109\/POV.2011.5712366"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2940","DOI":"10.1016\/j.patcog.2013.04.004","article-title":"Interactive image segmentation based on synthetic graph coordinates","volume":"46","author":"Panagiotakis","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.patcog.2009.03.004","article-title":"Interactive image segmentation by maximal similarity based region merging","volume":"43","author":"Ning","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Veksler, O. (2008, January 12\u201318). Star shape prior for graph-cut image segmentation. Proceedings of the European Conference on Computer Vision, Marseille, France.","DOI":"10.1007\/978-3-540-88690-7_34"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gulshan, V., Rother, C., Criminisi, A., Blake, A., and Zisserman, A. (2010, January 13\u201318). Geodesic star convexity for interactive image segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540073"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.patcog.2018.03.010","article-title":"Saliency driven region-edge-based top down level set evolution reveals the asynchronous focus in image segmentation","volume":"80","author":"Zhi","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_32","unstructured":"Xu, C., Yezzi, A., and Prince, J.L. (November, January 29). On the relationship between parametric and geometric active contours. Proceedings of the IEEE Conference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yu, Y., Zhang, C., Wei, Y., and Li, X. (2010, January 28\u201330). Active contour method combining local fitting energy and global fitting energy dynamically. Proceedings of the International Conference on Medical Biometrics, Hong Kong, China.","DOI":"10.1007\/978-3-642-13923-9_17"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.ijleo.2017.05.031","article-title":"Hybrid active contour model based on edge gradients and regional multi-features for infrared image segmentation","volume":"140","author":"Wan","year":"2017","journal-title":"Opt. Int. J. Light Electron. Opt."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1007\/s10043-016-0190-1","article-title":"An adaptive multi-feature segmentation model for infrared image","volume":"23","author":"Zhang","year":"2016","journal-title":"Opt. Rev."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1080\/09500340.2018.1426796","article-title":"Infrared small target enhancement: Grey level mapping based on improved sigmoid transformation and saliency histogram","volume":"65","author":"Wan","year":"2018","journal-title":"J. Mod. Opt."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1109\/34.56205","article-title":"Scale-space and edge detection using anisotropic diffusion","volume":"12","author":"Perona","year":"1990","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wan, M., Gu, G., Qian, W., Ren, K., Chen, Q., and Maldague, X. (2018). Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction. Remote Sens., 10.","DOI":"10.3390\/rs10050682"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Mangale, S.A., and Khambete, M.B. (2018). Approach for moving object detection using visible spectrum and thermal infrared imaging. J. Electron. Imaging, 27.","DOI":"10.1117\/1.JEI.27.3.033004"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1109\/TPAMI.2016.2562626","article-title":"Salient object detection via structured matrix decomposition","volume":"39","author":"Peng","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","unstructured":"(2018, June 16). Database Collection of Infrared Image. Available online: http:\/\/www.dgp.toronto.edu\/nmorris\/IR\/."},{"key":"ref_42","unstructured":"(2018, June 19). MSRA10K Salient Object Database. Available online: http:\/\/mmcheng.net\/msra10k\/."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.infrared.2016.03.019","article-title":"Polynomial fitting-based shape matching algorithm for multi-sensors remote sensing images","volume":"76","author":"Gu","year":"2016","journal-title":"Infrared Phys. Technol."},{"key":"ref_44","unstructured":"Gao, W., Zhang, X., Yang, L., and Liu, H. (2010, January 9\u201311). An improved Sobel edge detection. Proceedings of the 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), Chengdu, China."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/7\/1039\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:10:56Z","timestamp":1760195456000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/7\/1039"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,2]]},"references-count":44,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2018,7]]}},"alternative-id":["rs10071039"],"URL":"https:\/\/doi.org\/10.3390\/rs10071039","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,2]]}}}