Infrared Small Target Detection via Nonconvex Tensor Tucker Decomposition With Factor Prior

Liu, Ting; Yang, Jungang; Li, Boyang; Wang, Yingqian; An, Wei

doi:10.1109/tgrs.2023.3298192

Cited by 12 publications

(3 citation statements)

References 57 publications

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“…In order to assess the advantages of the TTALP-TV method, we compare it with eight representative baseline methods. These methods can be categorized into background consistency-based methods (Top-hat [7]), HVS-based methods (TLLCM [16]), LRSD-based single-frame detection methods (IPI [22], PSTNN [31], NTFRA [32], and ANLPT [33]) and LRSD-based sequential-frame detection methods (ASTTV-NTLA [63] and NFTDGSTV [42]). Table 3 lists the detailed parameter settings of these methods.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

“…Wang et al [41] integrated the nonoverlapping patch spatial-temporal tensor model (NPSTT) and the tensor capped nuclear norm (TCNN) for detection results with low false alarms. Further, Liu et al [42] designed a nonconvex tensor Tucker decomposition method, in which factor prior was used to obtain accurate background estimation and reduce computational complexity.…”

mentioning

confidence: 99%

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Infrared Small Target Detection Based on Tensor Tree Decomposition and Self-Adaptive Local Prior

Zhang,

Ding,

et al. 2024

Remote Sensing

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Infrared small target detection plays a crucial role in both military and civilian systems. However, current detection methods face significant challenges in complex scenes, such as inaccurate background estimation, inability to distinguish targets from similar non-target points, and poor robustness across various scenes. To address these issues, this study presents a novel spatial–temporal tensor model for infrared small target detection. In our method, we introduce the tensor tree rank to capture global structure in a more balanced strategy, which helps achieve more accurate background estimation. Meanwhile, we design a novel self-adaptive local prior weight by evaluating the level of clutter and noise content in the image. It mitigates the imbalance between target enhancement and background suppression. Then, the spatial–temporal total variation (STTV) is used as a joint regularization term to help better remove noise and obtain better detection performance. Finally, the proposed model is efficiently solved by the alternating direction multiplier method (ADMM). Extensive experiments demonstrate that our method achieves superior detection performance when compared with other state-of-the-art methods in terms of target enhancement, background suppression, and robustness across various complex scenes. Furthermore, we conduct an ablation study to validate the effectiveness of each module in the proposed model.

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Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Infrared Small Target Detection Based on Tensor Tree Decomposition and Self-Adaptive Local Prior

Zhang,

Ding,

et al. 2024

Remote Sensing

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“…; Zhang et al [27] apply l p -norm constraint to construct sparser target images. To reduce the computational complexity, the tensor is used to extend the data dimensionality [28]- [30]. For example, Dai et al [31] propose infrared patch-tensor model based on spatial correlation to suppress remaining edges.…”

Section: A Infrared Small Target Detectionmentioning

confidence: 99%

Robust Unsupervised Multifeature Representation for Infrared Small Target Detection

Chen,

Wu,

Zheng

et al. 2024

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

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Infrared small target detection is critical to infrared search and tracking (IRST) systems. However, accurate and robust detection remains challenging due to the scarcity of target information and the complexity of clutter interference. Existing methods have some limitations in feature representation, leading to poor detection performance in complex scenes. Especially when there are sharp edges near the target or in cluster multitarget detection, the "target suppression" phenomenon tends to occur. To address this issue, we propose a robust unsupervised multi-feature representation (RUMFR) method for infrared small target detection. On the one hand, robust unsupervised spatial clustering (RUSC) is designed to improve the accuracy of feature extraction; on the other hand, pixel-level multiple feature representation is proposed to fully utilize the target detail information. Specifically, we first propose the center-weighted interclass difference measure (CWIDM) with a trilayer design for fast candidate target extraction. Note that CWIDM also guides the parameter settings of RUSC. Then, the RUSC-based model is constructed to accurately extract target features in complex scenes. By designing the parameter adaptive strategy and iterative clustering strategy, RUSC can robustly segment cluster multi-targets from complex backgrounds. Finally, RUMFR that fuses pixel-level contrast, distribution and directional gradient features is proposed for better target representation and clutter suppression. Extensive experimental results show that our method has stronger feature representation capability and achieves better detection performance than several state-of-the-art methods.

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Spatiotemporal Representation Learning on Event Stream

Yang,

Li,

Jiang

et al. 2024

2024 7th International Symposium on Autonomous Systems (ISAS)

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Infrared Small Target Detection via Nonconvex Tensor Tucker Decomposition With Factor Prior

Cited by 12 publications

References 57 publications

Infrared Small Target Detection Based on Tensor Tree Decomposition and Self-Adaptive Local Prior

Infrared Small Target Detection Based on Tensor Tree Decomposition and Self-Adaptive Local Prior

Robust Unsupervised Multifeature Representation for Infrared Small Target Detection

Spatiotemporal Representation Learning on Event Stream

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