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

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...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2023-07, Vol.15 (14), p.3513
Main Authors: Shao, Yu, Kang, Xu, Ma, Mingyang, Chen, Cheng, He, Sun, Wang, Dejiang
Format: Article
Language:English
Subjects:
Algorithms
alternating direction method of multipliers
Approximation
Comparative analysis
False alarms
Image processing
Infrared imaging
infrared small target detection
L1–L2 norm
Mathematical analysis
Methods
nonconvex optimization
Optimization
Principal components analysis
Remote sensing
Target acquisition
Target detection
Variation
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Summary: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).
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15143513