Infrared small target detection using kernel low-rank approximation and regularization terms for constraints

Yan, Fei; Xu, Guili; Wu, Quan Wei; Wang, Junpu; Li, Zhenhua

doi:10.1016/j.infrared.2022.104222

Cited by 12 publications

(4 citation statements)

References 49 publications

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“…The data structure-based methods, for instance infrared patch-image (IPI) model [18], total variation weighted lowrank constraint method [19], and kernel robust principal component analysis model [20], distinguish infrared targets from background according to their different structural features, such as the sparsity of the target and low-rank of the background. Zhang et al [21] designed a model based on nonconvex rank approximation minimization joint norm (NRAM) to improve the detection ability of infrared small targets in complex background.…”

Section: Atraditional Methodsmentioning

confidence: 99%

Context-Guided Reverse Attention Network With Multiscale Aggregation for Infrared Small Target Detection

Zhong,

Zhang,

Duan

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Infrared small target detection is one of the vital tasks in various infrared detection application, and has some typical challenges such as small and dim target, background noise, and complex scenes. To address the problem, a context guided reverse attention network (CgraNet) is proposed to detect infrared small target by introducing context guided module (CGM), multiscale aggregation block (MAB), and reverse attention module (RAM). The CGM is designed to capture inherent property of semantic information from multi-scale encode layer in pixel-level recognition. In order to eliminate the impact of low-level feature on computational complexity and ensure the detection performance, we design the MAB to aggregate multiscale feature. The RAM is integrated in decoder layer to combine the features from MAB and CGM for fusing the localization information and multiscale structural information. Extensive experiments on infrared small target datasets demonstrate that our method can achieve high detection accuracy and low false alarm rate compared with some state-of-the-art model-driven and datadriven methods.

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Section: Atraditional Methodsmentioning

confidence: 99%

Context-Guided Reverse Attention Network With Multiscale Aggregation for Infrared Small Target Detection

Zhong,

Zhang,

Duan

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…On one hand, some methods have used prior constraints, including Column-Weighted IPI (WIPI) [18], Non-negative IPI with Partial Sum (NIPPS) [20], and Re-Weighted IPI (ReWIPI) [21]. On the other hand, some studies have identified limitations in the nuclear norm and L1 norm and, so, alternative norms to achieve improved target representation and background suppression have been proposed; for example, Non-convex Rank Approximation Minimization (NRAM) [22] and Non-convex Optimization with Lp norm Constraint (NOLC) [23] introduce non-convex matrix rank approximation coupled with L2,1 norm and Lp norm regularization, while Total Variation Weighted Low-Rank (TVWLR) [24], Kernel Robust Principal Component Analysis (KRPCA) [25] introduce total variation regularization, High Local Variance (HLV) [26] method present LV* norm to constrain the background's local variance. Patch-based methods mainly consider the low-rank nature of the background, affecting their performance in the presence of strong edges.…”

Section: Patch-based Methodsmentioning

confidence: 99%

“…The most popular method is Infrared Patch-Image (IPI) [17], which uses a sliding window technique to generate a corresponding patch image from the original image. Due to its outstanding performance, many studies [18][19][20][21][22][23][24][25][26] have been conducted on IPI, which typically yields superior results. However, patch-based methods still have two problems: (1) The misclassification of strong edges as sparse target components, and (2) the time-consuming nature of the method.…”

Section: Introductionmentioning

confidence: 99%

Infrared Small-Target Detection Based on Background-Suppression Proximal Gradient and GPU Acceleration

Hao,

Liu,

Liu

et al. 2023

Remote Sensing

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Patch-based methods improve the performance of infrared small target detection, transforming the detection problem into a Low-Rank Sparse Decomposition (LRSD) problem. However, two challenges hinder the success of these methods: (1) The interference from strong edges of the background, and (2) the time-consuming nature of solving the model. To tackle these two challenges, we propose a novel infrared small-target detection method using a Background-Suppression Proximal Gradient (BSPG) and GPU parallelism. We first propose a new continuation strategy to suppress the strong edges. This strategy enables the model to simultaneously consider heterogeneous components while dealing with low-rank backgrounds. Then, the Approximate Partial Singular Value Decomposition (APSVD) is presented to accelerate solution of the LRSD problem and further improve the solution accuracy. Finally, we implement our method on GPU using multi-threaded parallelism, in order to further enhance the computational efficiency of the model. The experimental results demonstrate that our method out-performs existing advanced methods, in terms of detection accuracy and execution time.

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“…However, in the presence of strong bright noise in the backgrounds, they may result in higher false alarm rates. LRM-based methods [16]- [20] treat the infrared image as a low-rank sparse matrix, and introduce the low-rank matrix reconstruction to filter the targets with the backgrounds. Since the intensity of the target is not significant with respect to the intensity of the backgrounds, these methods do not detect targets under various shapes well.…”

Section: Introductionmentioning

confidence: 99%

Toward Accurate Infrared Small Target Detection via Edge-Aware Gated Transformer

Zhu,

Ma,

Fan

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Extracting small targets from complex backgrounds is the eventual goal of single infrared small target detection (SISTD), which has many potential applications in defense security and marine rescue. Recently, methods utilizing deep learning have shown their superiority over traditional theoretical approaches. However, they do not consider both the global semantics and specific shape information, thereby limiting their performance. To overcome this proplem, we propose a Gated Shaped TransUnet (GSTUnet), designed to fully utilize shape information while detecting small target detection. Specifically, we have proposed a multi-scale encoder branch to extract global features of small targets at different scales. Then, the extracted global features are passed through a gated-shaped stream branch that focuses on the shape information of small targets through gate convolutions. Finally, we fuse there features to obtain the final result. Our GSTUnet learns both global and shape information through the aforementioned two branches, establishing global relationships between different feature scales. The GSTUnet demonstrates excellent evaluation metrics on various datasets, outperforming current state-of-the-art methods. To access our code and datasets, please visit the following link: https://github.com/

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Infrared small target detection using kernel low-rank approximation and regularization terms for constraints

Cited by 12 publications

References 49 publications

Context-Guided Reverse Attention Network With Multiscale Aggregation for Infrared Small Target Detection

Context-Guided Reverse Attention Network With Multiscale Aggregation for Infrared Small Target Detection

Infrared Small-Target Detection Based on Background-Suppression Proximal Gradient and GPU Acceleration

Toward Accurate Infrared Small Target Detection via Edge-Aware Gated Transformer

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