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This work focuses on military vehicle detection using Synthetic Aperture Radar (SAR) images from the MSTAR dataset. Challenges such as speckle noise, limited data size, and classification accuracy are addressed using preprocessing techniques, dataset augmentation via Spectral Normalization GANs (SN-GANs), and a custom-designed Convolutional Neural Network (CNN). The proposed methodology achieves an accuracy of 98.1%, showcasing the potential of GAN-augmented SAR datasets in target recognition tasks.

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Target Recognition Using Synthetic Aperture Radar (SAR) Imagery

  • 会议论文

  • 发表日期：

    2026年

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  • S. Santhameena,
  • Shaunak Agrawal,
  • Shobith R. Prabhu,
  • Shaurishail M. Awanti,
  • Siddharaj Dhegaskar

摘要

This work focuses on military vehicle detection using Synthetic Aperture Radar (SAR) images from the MSTAR dataset. Challenges such as speckle noise, limited data size, and classification accuracy are addressed using preprocessing techniques, dataset augmentation via Spectral Normalization GANs (SN-GANs), and a custom-designed Convolutional Neural Network (CNN). The proposed methodology achieves an accuracy of 98.1%, showcasing the potential of GAN-augmented SAR datasets in target recognition tasks.