Volume 13 Issue 2
Apr.  2024
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Article Navigation >  Journal of Radars  > 2024  >  13(2): 359-373
LI Miaoge, CHEN Bo, WANG Dongsheng, et al. CNN model visualization method for SAR image target classification[J]. Journal of Radars, 2024, 13(2): 359–373. doi: 10.12000/JR23107
Citation: LI Miaoge, CHEN Bo, WANG Dongsheng, et al. CNN model visualization method for SAR image target classification[J]. Journal of Radars, 2024, 13(2): 359–373. doi: 10.12000/JR23107
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CNN Model Visualization Method for SAR Image Target Classification

doi: 10.12000/JR23107

  • National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds:  The National Natural Science Foundation of China (U21B2006), Shaanxi Youth Innovation Team Project, The Fundamental Research Funds for the Central Universities (QTZX23037, QTZX22160), The 111 Project (B18039)
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  • Corresponding author: CHEN Bo, bchen@mail.xidian.edu.cn
  • Received Date: 2023-06-16
  • Rev Recd Date: 2023-09-24
    • Available Online: 2023-09-27
  • Publish Date: 2023-10-20
    Abstract
  • Convolutional Neural Network (CNN) is widely used for image target classifications in Synthetic Aperture Radar (SAR), but the lack of mechanism transparency prevents it from meeting the practical application requirements, such as high reliability and trustworthiness. The Class Activation Mapping (CAM) method is often used to visualize the decision region of the CNN model. However, existing methods are primarily based on either channel-level or space-level class activation weights, and their research progress is still in its infancy regarding more complex SAR image datasets. Based on this, this paper proposes a CNN model visualization method for SAR images, considering the feature extraction ability of neurons and their current network decisions. Initially, neuronal activation values are used to visualize the capability of neurons to learn a target structure in its corresponding receptive field. Further, a novel CAM-based method combined with channel-wise and spatial-wise weights is proposed, which can provide the foundation for the decision-making process of the trained CNN models by detecting the crucial areas in SAR images. Experimental results showed that this method provides interpretability analysis of the model under different settings and effectively expands the application of CNNs for SAR image visualization.

     

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