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Infrared Small Target Detection Based on Interpretation Weighted Sparse Method
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AI
DOI：10.1109/TGRS.2025.3532215.png)
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Abstract
En 中文
Accurately and robustly detecting infrared (IR) small targets plays an essential role in IR search and tracking (IRST) systems against complex backgrounds. However, the low signal-to-clutter ratio (SCR) feature target, also known as the dim target, is more similar to the local background region, making it prone to being misdetected as background or buried in clutter interferences, ultimately leading to detection failure. This has become a common and unavoidable challenge in the field. To alleviate this issue, an interpretation weighted sparse (IWS) method is proposed to detect IR small and dim targets while maintaining clutter suppression capability. The interpretability operation of the proposed method stems from an analysis of IR target characteristics, including dim target feature extraction and clutter suppression. Based on the multisubspaces model, the relevant feature of the dim target is mined by a designed structure tensor, improving the target's sparsity. Then, the contrast characteristic is introduced to eliminate the cost of dim target mine operation, namely residual clutter interference. Finally, the IWS map is obtained by fusing the target detection module and clutter suppression module, after which an adaptive threshold segmentation operation is adopted to extract the small targets. A series of experimental results and application discussion cases demonstrate that IWS outperforms other existing methods regarding detection performance while also exhibiting background suppression ability.
Keywords:
Object detection
Clutter
Feature extraction
Robustness
Filters
Tensors
Target tracking
Shape
Principal component analysis
Image edge detection
Background suppression
dim target
infrared (IR) small target detection
interpretation weighted sparse (IWS)
structure tensor
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