Infrared Small Target Detection via Multidirectional Local Gravitational Force and Level-Line Connectivity

Infrared small target detection is significantly challenged by residual high-intensity background edges and a low signal-to-noise ratio. These issues hinder accurate target differentiation from the background and heighten the risk of false alarms. To address these challenges, we propose a method tha...

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Vydáno v:	IEEE journal of selected topics in applied earth observations and remote sensing Ročník 18; s. 11111 - 11127
Hlavní autoři:	Hao, Xuying, Liu, Xianyuan, Liu, Yujia, Qiu, Yijuan, Zhang, Yunjing, Cui, Yi, Lei, Tao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accuracy Adaptation models Background noise Earth False alarms Feature extraction Gravity Gray-scale Image edge detection Information processing Infrared small target detection (IRSTD) level-line local gravitational force (LGF) Noise reduction Object detection Polynomials Remote sensing Signal to noise ratio Target detection
ISSN:	1939-1404, 2151-1535
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Abstract	Infrared small target detection is significantly challenged by residual high-intensity background edges and a low signal-to-noise ratio. These issues hinder accurate target differentiation from the background and heighten the risk of false alarms. To address these challenges, we propose a method that employs multidirectional local gravitational force (LGF) contrast combined with level-line connectivity (LLC) contrast. The LGF model integrates information from each pixel within the local region and introduces a new sigmoid function to reduce noise, enabling fine-grained gradient detection. The magnitude and orientation in this gradient can then be used to differentiate the target from the background. Considering that the target exhibits different gradient features in different directions, we further propose a multidirectional LGF contrast. This contrast utilizes the distribution characteristics of LGF magnitude to enhance the target and effectively suppress strong edges. In addition, to fully utilize the orientation information in the LGF, we designed the LLC contrast based on the spatial consistency of the target, increasing the difference between the target and the background. Finally, we propose a regional fusion technique to weight the two contrasts, improving background suppression while preserving target intensity. Experimental results demonstrate the effectiveness of our method in detecting targets within high-intensity edge backgrounds, complex textures, and noisy environments. Compared to other state-of-the-art methods, our method significantly improves detection accuracy.
AbstractList	Infrared small target detection is significantly challenged by residual high-intensity background edges and a low signal-to-noise ratio. These issues hinder accurate target differentiation from the background and heighten the risk of false alarms. To address these challenges, we propose a method that employs multidirectional local gravitational force (LGF) contrast combined with level-line connectivity (LLC) contrast. The LGF model integrates information from each pixel within the local region and introduces a new sigmoid function to reduce noise, enabling fine-grained gradient detection. The magnitude and orientation in this gradient can then be used to differentiate the target from the background. Considering that the target exhibits different gradient features in different directions, we further propose a multidirectional LGF contrast. This contrast utilizes the distribution characteristics of LGF magnitude to enhance the target and effectively suppress strong edges. In addition, to fully utilize the orientation information in the LGF, we designed the LLC contrast based on the spatial consistency of the target, increasing the difference between the target and the background. Finally, we propose a regional fusion technique to weight the two contrasts, improving background suppression while preserving target intensity. Experimental results demonstrate the effectiveness of our method in detecting targets within high-intensity edge backgrounds, complex textures, and noisy environments. Compared to other state-of-the-art methods, our method significantly improves detection accuracy.
Author	Hao, Xuying Zhang, Yunjing Liu, Yujia Lei, Tao Liu, Xianyuan Qiu, Yijuan Cui, Yi
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SubjectTerms	Accuracy Adaptation models Background noise Earth False alarms Feature extraction Gravity Gray-scale Image edge detection Information processing Infrared small target detection (IRSTD) level-line local gravitational force (LGF) Noise reduction Object detection Polynomials Remote sensing Signal to noise ratio Target detection
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Title	Infrared Small Target Detection via Multidirectional Local Gravitational Force and Level-Line Connectivity
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