Infrared Dim Small Target Detection Based on Nonconvex Constraint with L1-L2 Norm and Total Variation

doi:10.3390/rs15143513

CORC > 长春光学精密机械与物理研究所 > 中国科学院长春光学精密机械与物理研究所

	Infrared Dim Small Target Detection Based on Nonconvex Constraint with L1-L2 Norm and Total Variation
	Y. Shao, X. Kang, M. Y. Ma, C. Chen, S. He and D. J. Wang
刊名	Remote Sensing
	2023
卷号	15 期号:14 页码:23
DOI	10.3390/rs15143513
英文摘要	Infrared dim small target detection has received a lot of attention, because it is a crucial component of the IR search and track systems (IRST). The robust principal component analysis (RPCA) is a common detection framework, which works with poor performance with complex background edges and sparse clutters due to the inappropriate approximation of sparse items. A nonconvex constraint detection method based on the difference between the L1 and L2 (L1-L2) norm and total variation (TV) is presented. The L1-L2 norm is a more accurate sparse item approximation of L0 norm, which can achieve a better description of the sparse item to separate the target from the complex backgrounds. Then, the total variation norm is conducted on the target image to suppress the sparse clutters. The new model is solved using the alternating direction method of multipliers (ADMM) method. Then, the subproblems in the model are tackled by the difference of convex algorithm (DCA) and the Newton conjugate gradient (Newton-CG) solving L1-L2 norm and TV norm, respectively. In the experiment, we conducted experiments on multiple and single target datasets, and the proposed model outperforms the state-of-the-art (SOTA) methods in terms of background suppression and robustness to accurately detect the target. It can achieve a higher true position rate (TPR) with a low false position rate (FPR).
URL标识	查看原文
语种	英语
内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/67832]
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Y. Shao, X. Kang, M. Y. Ma, C. Chen, S. He and D. J. Wang. Infrared Dim Small Target Detection Based on Nonconvex Constraint with L1-L2 Norm and Total Variation[J]. Remote Sensing,2023,15(14):23.
APA	Y. Shao, X. Kang, M. Y. Ma, C. Chen, S. He and D. J. Wang.(2023).Infrared Dim Small Target Detection Based on Nonconvex Constraint with L1-L2 Norm and Total Variation.Remote Sensing,15(14),23.
MLA	Y. Shao, X. Kang, M. Y. Ma, C. Chen, S. He and D. J. Wang."Infrared Dim Small Target Detection Based on Nonconvex Constraint with L1-L2 Norm and Total Variation".Remote Sensing 15.14(2023):23.