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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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing Vol. 18; pp. 11111 - 11127
Main Authors: Hao, Xuying, Liu, Xianyuan, Liu, Yujia, Qiu, Yijuan, Zhang, Yunjing, Cui, Yi, Lei, Tao
Format: Journal Article
Language:English
Published: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2025.3560306