Volume 13 Issue 2
Apr.  2024
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CUI Zongyong, YANG Zhiyuan, JIANG Yang, et al. Explainability of deep networks for SAR target recognition via class activation mapping[J]. Journal of Radars, 2024, 13(2): 428–442. doi: 10.12000/JR23188
Citation: CUI Zongyong, YANG Zhiyuan, JIANG Yang, et al. Explainability of deep networks for SAR target recognition via class activation mapping[J]. Journal of Radars, 2024, 13(2): 428–442. doi: 10.12000/JR23188

Explainability of Deep Networks for SAR Target Recognition via Class Activation Mapping

doi: 10.12000/JR23188
Funds:  The National Natural Science Foundation of China (62271116, 61971101)
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  • Corresponding author: CAO Zongjie, zjcao@uestc.edu.cn
  • Received Date: 2023-10-04
  • Rev Recd Date: 2024-01-13
  • Available Online: 2024-01-18
  • Publish Date: 2024-02-05
  • With the widespread application of deep learning methods in Synthetic Aperture Radar (SAR) image interpretation, the explainability of SAR target recognition deep networks has gradually attracted the attention of scholars. Class Activation Mapping (CAM), a commonly used explainability algorithm, can visually display the salient regions influencing the recognition task through heatmaps. However, as a post hoc explanation method, CAM can only statically display the salient regions during the current recognition process and cannot dynamically show the variation patterns of the salient regions upon changing the input. This study introduces the concept of perturbation into CAM, proposing an algorithm called SAR Clutter Characteristics CAM (SCC-CAM). By introducing globally distributed perturbations to the input image, interference is gradually applied to deep SAR recognition networks, causing decision flips. The degree of change in the activation values of network neurons is also calculated. This method addresses the issue of perturbation propagation and allows for dynamic observation and measurement of variation patterns of salient regions during the recognition process. Thus, SCC-CAM enhances the explainability of deep networks. Experiments on the MSTAR and OpenSARShip-1.0 datasets demonstrate that the proposed algorithm can more accurately locate salient regions. Compared with traditional methods, the algorithm in this study shows stronger explainability in terms of average confidence degradation rates, confidence ascent ratios, information content, and other evaluation metrics. This algorithm can serve as a universal method for enhancing the explainability of networks.

     

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