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CG<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>OSR: A Class Gaussian Guided Framework for SAR Open-Set Target Recognition
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DOI：10.1109/TAES.2025.3618812.png)
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Abstract
En 中文
Synthetic aperture radar (SAR) target recognition has achieved significant success in closed-set tasks. However, for real-world applications, SAR systems should not only accurately recognize targets of known classes but also have the ability to detect targets of unknown classes. To this end, we propose a generative class Gaussian guided open-set recognition (OSR) framework to improve SAR OSR performance. First, we design a Kullback–Leibler divergence classifier to recognize known classes. It directly uses the discrepancy between the input distribution and the class distributions learned in the generative phase. The design of classifier integrates the process of generating and recognizing, making the recognition process in the generative OSR framework more streamlined. Then, we propose a reconstructive-matching detector for unknown-class detection. The detector simulates the properties of SAR images from unknown classes by constructing unmatched SAR image pairs. Based on this, the reconstructive-matching mechanism is able to mimic open-set image-matching patterns using closed-set SAR data. The effective decision boundary for unknown-class detection can be provided through the statistical distribution of recognition results during training. Finally, a dual-logits module is designed to reevaluate the likelihood of test SAR images from known classes and to correct closed-set recognition probabilities, thereby improving SAR OSR performance. Experimental results demonstrate that the proposed method achieves superior SAR OSR performance over existing state-of-the-art methods.
Keywords:
Class Gaussian
dual-logits
Kullback–Leibler divergence (KLD) classifier
open-set recognition (OSR)
reconstructive-matching detector
synthetic aperture radar (SAR)
Journal
IF:
5.7
Papers:
682
Citations:
2.4W