Infrared Small Dim Target Detection Using Group Regularized Principle Component Pursuit

被引:2
作者
Li, Meihui [1 ,2 ,3 ,4 ]
Wei, Yuxing [1 ,2 ,3 ,4 ]
Dan, Bingbing [1 ,2 ,3 ,4 ]
Liu, Dongxu [1 ,2 ,3 ]
Zhang, Jianlin [1 ,2 ,3 ,4 ]
机构
[1] Chinese Acad Sci, Inst Opt & Elect, Chengdu 610209, Peoples R China
[2] Chinese Acad Sci, Natl Key Lab Opt Field Manipulat Sci & Technol, Chengdu 610209, Peoples R China
[3] Chinese Acad Sci, Key Lab Opt Engn, Chengdu 610209, Peoples R China
[4] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
基金
中国国家自然科学基金;
关键词
infrared small target detection; principle component pursuit; group low-rank regularization; infrared patch-image model; MODEL;
D O I
10.3390/rs16010016
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The detection of an infrared small target faces the problems of background interference and non-obvious target features, which have yet to be efficiently solved. By employing the non-local self-correlation characteristic of the infrared images, the principle component pursuit (PCP)-based methods are demonstrated to be applicable to infrared small target detection in a complex scene. However, existing PCP-based methods heavily depend on the uniform distribution of the background pixels and are prone to generating a high number of false alarms under strong clutter situations. In this paper, we propose a group low-rank regularized principle component pursuit model (GPCP) to solve this problem. First, the local image patches are clustered into several groups that correspond to different grayscale distributions. These patch groups are regularized with a group low-rank constraint, enabling an independent recovery of different background regions. Then, GPCP model integrates the group low-rank components with a global sparse component to extract small targets from the background. Different singular value thresholds can be exploited for image groups corresponding to different brightness and grayscale variance, boosting the recovery of background clutters and also enhancing the detection of small targets. Finally, a customized optimization approach based on alternating direction method of multipliers is proposed to solve this model. We set three representative detection scenes, including the ground background, sea background and sky background for experiment analysis and model comparison. The evaluation results show the proposed model has superiority in background suppression and achieves better adaptability for different scenes compared with various state-of-the-art methods.
引用
收藏
页数:21
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