{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T15:38:29Z","timestamp":1775057909916,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,29]],"date-time":"2023-04-29T00:00:00Z","timestamp":1682726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Open Foundation of Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security","award":["62071303"],"award-info":[{"award-number":["62071303"]}]},{"name":"Open Foundation of Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security","award":["62201355"],"award-info":[{"award-number":["62201355"]}]},{"name":"Open Foundation of Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security","award":["2021M702275"],"award-info":[{"award-number":["2021M702275"]}]},{"name":"Open Foundation of Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security","award":["JCYJ20190808151615540"],"award-info":[{"award-number":["JCYJ20190808151615540"]}]},{"name":"the National Natural Science Foundation of China","award":["62071303"],"award-info":[{"award-number":["62071303"]}]},{"name":"the National Natural Science Foundation of China","award":["62201355"],"award-info":[{"award-number":["62201355"]}]},{"name":"the National Natural Science Foundation of China","award":["2021M702275"],"award-info":[{"award-number":["2021M702275"]}]},{"name":"the National Natural Science Foundation of China","award":["JCYJ20190808151615540"],"award-info":[{"award-number":["JCYJ20190808151615540"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["62071303"],"award-info":[{"award-number":["62071303"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["62201355"],"award-info":[{"award-number":["62201355"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2021M702275"],"award-info":[{"award-number":["2021M702275"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["JCYJ20190808151615540"],"award-info":[{"award-number":["JCYJ20190808151615540"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Science and Technology Projection","award":["62071303"],"award-info":[{"award-number":["62071303"]}]},{"name":"Shenzhen Science and Technology Projection","award":["62201355"],"award-info":[{"award-number":["62201355"]}]},{"name":"Shenzhen Science and Technology Projection","award":["2021M702275"],"award-info":[{"award-number":["2021M702275"]}]},{"name":"Shenzhen Science and Technology Projection","award":["JCYJ20190808151615540"],"award-info":[{"award-number":["JCYJ20190808151615540"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the rapid development of marine business, the intelligent detection of ship targets has become the key to marine safety. However, it is difficult to accurately detect maritime infrared targets due to severe sea clutter interference in strong wind waves or dim sea scenes. To adapt to diverse marine environments, a dual-mode sea background model is proposed for target detection. According to the global contrast of the image, the scene is divided into the sea surface with violent changes and the sea surface with stable changes. In the first stage, the preliminary background model suitable for steadily changing scenes is proposed. The pixel-level foreground mask is generated through the background block filter and the posterior probability criterion. Moreover, the learning rate parameter is adjusted using the detection results of two adjacent frames. In the second stage, the background model suitable for highly fluctuating scenes is proposed. Moreover, the local correlation feature is used to enhance the local contrast of the frame. The experimental results for the different scenes show that the proposed method has a better detection performance than the other comparison algorithms.<\/jats:p>","DOI":"10.3390\/rs15092354","type":"journal-article","created":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T12:10:03Z","timestamp":1682943003000},"page":"2354","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Maritime Infrared Target Detection Using a Dual-Mode Background Model"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9537-101X","authenticated-orcid":false,"given":"Anran","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Computer Science, Chengdu University, Chengdu 610106, China"},{"name":"Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, Shenzhen 518060, China"}]},{"given":"Weixin","family":"Xie","sequence":"additional","affiliation":[{"name":"Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, Shenzhen 518060, China"},{"name":"College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518061, China"}]},{"given":"Jihong","family":"Pei","sequence":"additional","affiliation":[{"name":"Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, Shenzhen 518060, China"},{"name":"College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518061, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1109\/TIV.2020.2991955","article-title":"Robust Detection of Infrared Maritime Targets for Autonomous Navigation","volume":"5","author":"Wang","year":"2020","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lu, Y., Dong, L., Zhang, T., and Xu, W. (2020). A Robust Detection Algorithm for Infrared Maritime Small and Dim Targets. Sensors, 20.","DOI":"10.3390\/s20041237"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Song, J., Xiong, W., Chen, X., and Lu, Y. (2022). Experimental Study of Maritime Moving Target Detection Using Hitchhiking Bistatic Radar. Remote Sens., 14.","DOI":"10.3390\/rs14153611"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1080\/02564602.2016.1231023","article-title":"Saliency Detection for Small Maritime Target Using Singular Value Decomposition of Amplitude Spectrum","volume":"34","author":"Ren","year":"2017","journal-title":"IETE Tech. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wu, J., Li, J., Li, R., Xi, X., Gui, D., and Yin, J. (2022). A Fast Maritime Target Identification Algorithm for Offshore Ship Detection. Appl. Sci., 12.","DOI":"10.3390\/app12104938"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.comcom.2019.01.002","article-title":"Efficient target detection in maritime search and rescue wireless sensor network using data fusion","volume":"136","author":"Wu","year":"2019","journal-title":"Comput. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.infrared.2015.01.031","article-title":"An infrared maritime target detection algorithm applicable to heavy sea fog","volume":"71","author":"Wang","year":"2015","journal-title":"Infrared Phys. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhao, E., Dong, L., and Dai, H. (2022). Infrared Maritime Small Target Detection Based on Multidirectional Uniformity and Sparse-Weight Similarity. Remote Sens., 14.","DOI":"10.3390\/rs14215492"},{"key":"ref_9","first-page":"1","article-title":"Robust Infrared Maritime Target Detection via Anti-Jitter Spatial\u2013Temporal Trajectory Consistency","volume":"19","author":"Yang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2839","DOI":"10.1016\/j.patcog.2014.03.005","article-title":"Iterative infrared ship target segmentation based on multiple features","volume":"47","author":"Liu","year":"2014","journal-title":"Pattern Recogn."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1109\/JSTARS.2021.3049847","article-title":"Infrared Small Maritime Target Detection Based on Integrated Target Saliency Measure","volume":"14","author":"Yang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103940","DOI":"10.1016\/j.infrared.2021.103940","article-title":"Infrared maritime small target detection based on edge and local intensity features","volume":"119","author":"Zhang","year":"2021","journal-title":"Infrared Phys. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.infrared.2017.12.018","article-title":"Infrared small target detection based on local intensity and gradient properties","volume":"89","author":"Hong","year":"2018","journal-title":"Infrared Phys. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"033003","DOI":"10.1117\/1.3456695","article-title":"Comparative study of background subtraction algorithms","volume":"19","author":"Benezeth","year":"2010","journal-title":"J. Electron. Imaging"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1109\/34.868677","article-title":"Learning patterns of activity using real-time tracking","volume":"22","author":"Stauffer","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.cviu.2014.01.004","article-title":"A self-adaptive Gaussian mixture model","volume":"122","author":"Chen","year":"2014","journal-title":"Comput.Vis. Image Understand."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1109\/TCSVT.2013.2243649","article-title":"Illumination-robust foreground detection in a video surveillance system","volume":"23","author":"Li","year":"2013","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5022","DOI":"10.1109\/TIP.2013.2281423","article-title":"Multiresolution based Gaussian mixture model for background suppression","volume":"22","author":"Mukherjee","year":"2013","journal-title":"IEEE Trans.Image Process."},{"key":"ref_19","unstructured":"Chen, Y., Wang, J., and Lu, H. (July, January 29). Learning sharable models for robust background subtraction. Proceedings of the IEEE International Conference on Multi-Media and Expo (ICME), Turin, Italy."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1109\/TPAMI.2013.239","article-title":"Background subtraction with Dirichlet process mixture models","volume":"36","author":"Haines","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2204","DOI":"10.1109\/TITS.2019.2917560","article-title":"Rapid and Robust Background Modeling Technique for Low-Cost Road Traffic Surveillance Systems","volume":"21","author":"Garg","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"168980","DOI":"10.1016\/j.ijleo.2022.168980","article-title":"A multi features based background modeling approach for moving object detection","volume":"260","author":"Moudgollya","year":"2022","journal-title":"Optik"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2634","DOI":"10.1109\/TCSVT.2019.2922036","article-title":"Background Modeling in the Fourier Domain for Maritime Infrared Target Detection","volume":"30","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_24","first-page":"1","article-title":"Background Modeling Combined With Multiple Features in the Fourier Domain for Maritime Infrared Target Detection","volume":"60","author":"Zhou","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1109\/TCSVT.2014.2361418","article-title":"Binary Descriptor Based Nonparametric Background Modeling for Foreground Extraction by Using Detection Theory","volume":"25","author":"Yang","year":"2015","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1709","DOI":"10.1109\/TIP.2010.2101613","article-title":"ViBe: A universal background subtraction algorithm for video sequences","volume":"20","author":"Barnich","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"St-Charles, P., and Bilodeau, G. (2014, January 24\u201326). Improving background subtraction using local binary similarity patterns. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Steamboat Springs, CO, USA.","DOI":"10.1109\/WACV.2014.6836059"},{"key":"ref_28","unstructured":"Martin, H., Philipp, T., and Gerhard, R. (2012, January 16\u201321). Background segmentation with feedback: The pixel-based adaptive segmenter. Proceedings of the IEEE Computer Software and Applications Conference on Computer Vision and Pattern Recognition Workshops (COMPSAC), Providence, RI, USA."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1109\/TIP.2014.2378053","article-title":"SuBSENSE: A universal change detection method with local adaptive sensitivity","volume":"24","author":"Bilodeau","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"9769","DOI":"10.1109\/ACCESS.2019.2891084","article-title":"Locally statistical dual-mode background subtraction approach","volume":"7","author":"Hua","year":"2019","journal-title":"IEEE Access."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.knosys.2018.10.031","article-title":"A general moving detection method using dual-target nonparametric background model","volume":"164","author":"Zhong","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Braham, M., and Droogenbroeck, M. (2016, January 23\u201325). Deep background subtraction with scene-specific convolutional neural networks. Proceedings of the International Conference on Systems Signals and Image Processing (IWSSIP), Bratislava, Slovakia.","DOI":"10.1109\/IWSSIP.2016.7502717"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1109\/TITS.2017.2754099","article-title":"Deep Background Modeling Using Fully Convolutional Network","volume":"19","author":"Yang","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"9478","DOI":"10.1109\/TVT.2019.2937076","article-title":"Video Foreground Extraction Using Multi-View Receptive Field and Encoder-Decoder DCNN for Traffic and Surveillance Applications","volume":"68","author":"Thangarajah","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1109\/LGRS.2018.2881053","article-title":"Foreground Detection for Infrared Videos with Multiscale 3-D Fully Convolutional Network","volume":"16","author":"Wang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.imavis.2015.12.005","article-title":"Infrared ship target segmentation through integration of multiple feature maps","volume":"48","author":"Liu","year":"2016","journal-title":"Image Vis. Comput."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2354\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:26:37Z","timestamp":1760124397000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2354"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,29]]},"references-count":36,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15092354"],"URL":"https:\/\/doi.org\/10.3390\/rs15092354","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,29]]}}}