Volume 13 Issue 2
Apr.  2024
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Article Navigation >  Journal of Radars  > 2024  >  13(2): 443-456
LI Yi, DU Lan, ZHOU Ke’er, et al. Deep network for SAR target recognition based on attribute scattering center convolutional kernel modulation[J]. Journal of Radars, 2024, 13(2): 443–456. doi: 10.12000/JR24001
Citation: LI Yi, DU Lan, ZHOU Ke’er, et al. Deep network for SAR target recognition based on attribute scattering center convolutional kernel modulation[J]. Journal of Radars, 2024, 13(2): 443–456. doi: 10.12000/JR24001
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Deep Network for SAR Target Recognition Based on Attribute Scattering Center Convolutional Kernel Modulation

DOI: 10.12000/JR24001

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

Funds:  The National Natural Science Foundation of China (U21B2039)
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  • Corresponding author: DU Lan, dulan@mail.xidian.edu.cn
  • Received Date: 2024-01-04
  • Rev Recd Date: 2024-03-13
    • Available Online: 2024-03-19
  • Publish Date: 2024-03-27
    Abstract
  • The feature extraction capability of Convolutional Neural Networks (CNNs) is related to the number of their parameters. Generally, using a large number of parameters leads to improved feature extraction capability of CNNs. However, a considerable amount of training data is required to effectively learn these parameters. In practical applications, Synthetic Aperture Radar (SAR) images available for model training are often limited. Reducing the number of parameters in a CNN can decrease the demand for training samples, but the feature expression ability of the CNN is simultaneously diminished, which affects its target recognition performance. To solve this problem, this paper proposes a deep network for SAR target recognition based on Attribute Scattering Center (ASC) convolutional kernel modulation. Given the electromagnetic scattering characteristics of SAR images, the proposed network extracts scattering structures and edge features that are more consistent with the characteristics of SAR targets by modulating a small number of CNN convolutional kernels using predefined ASC kernels with different orientations and lengths. This approach generates additional convolutional kernels, which can reduce the network parameters while ensuring feature extraction capability. In addition, the designed network uses ASC-modulated convolutional kernels at shallow layers to extract scattering structures and edge features that are more consistent with the characteristics of SAR images while utilizing CNN convolutional kernels at deeper layers to extract semantic features of SAR images. The proposed network focuses on the electromagnetic scattering characteristics of SAR targets and shows the feature extraction advantages of CNNs due to the simultaneous use of ASC-modulated and CNN convolutional kernels. Experiments based on the studied SAR images demonstrate that the proposed network can ensure excellent SAR target recognition performance while reducing the demand for training samples.

     

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