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<p>By integrating multi-level wavelet decomposition into the tokenization stage of Vision Transformers, we introduce a frequency-aware representation framework tailored for synthetic aperture radar (SAR) automatic target recognition (ATR). The proposed Wavelet-Patch Vision Transformer++ (WP-ViT++) decomposes SAR images into multi-resolution frequency sub-bands, enabling explicit separation of global structural information and high-frequency scattering features. Through wavelet-domain denoising and sub-band token embedding, the model enhances robustness against speckle noise while preserving discriminative target characteristics. A cross-wavelet attention mechanism further enables joint modeling of spatial–frequency dependencies, improving the representation of complex SAR signatures. Unlike conventional transformer-based approaches that rely solely on spatial patches, the proposed method incorporates domain-aligned frequency priors, leading to more stable and noise-resilient feature learning. Experimental results on the MSTAR benchmark demonstrate that WP-ViT++ achieves 93.6%, classification accuracy, outperforming ViT, SpectFormer-Lite, and DiffFormer-Lite by significant margins. In addition, the proposed model maintains strong performance under noise perturbations, achieving over 93%, accuracy under speckle noise conditions. These results confirm that wavelet-enhanced tokenization provides an effective and scalable solution for robust SAR ATR, improving both classification accuracy and generalization without increasing architectural complexity.</p>

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WP-ViT2Level: Multi-level wavelet-patch vision transformers for Robust SAR automatic target recognition

  • 期刊论文

  • 发表日期：

    2026年7月24日

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  • Aisha Sir Elkhatem,
  • Yerbol Ospanov,
  • Madina Kamet,
  • Aizhan Erulanova

摘要

By integrating multi-level wavelet decomposition into the tokenization stage of Vision Transformers, we introduce a frequency-aware representation framework tailored for synthetic aperture radar (SAR) automatic target recognition (ATR). The proposed Wavelet-Patch Vision Transformer++ (WP-ViT++) decomposes SAR images into multi-resolution frequency sub-bands, enabling explicit separation of global structural information and high-frequency scattering features. Through wavelet-domain denoising and sub-band token embedding, the model enhances robustness against speckle noise while preserving discriminative target characteristics. A cross-wavelet attention mechanism further enables joint modeling of spatial–frequency dependencies, improving the representation of complex SAR signatures. Unlike conventional transformer-based approaches that rely solely on spatial patches, the proposed method incorporates domain-aligned frequency priors, leading to more stable and noise-resilient feature learning. Experimental results on the MSTAR benchmark demonstrate that WP-ViT++ achieves 93.6%, classification accuracy, outperforming ViT, SpectFormer-Lite, and DiffFormer-Lite by significant margins. In addition, the proposed model maintains strong performance under noise perturbations, achieving over 93%, accuracy under speckle noise conditions. These results confirm that wavelet-enhanced tokenization provides an effective and scalable solution for robust SAR ATR, improving both classification accuracy and generalization without increasing architectural complexity.