Studying the Effects of Self-Attention on SAR Automatic Target Recognition

TL;DR

It is shown that attention modules increase top-1 accuracy, improve input robustness, and are qualitatively more explainable on the MSTAR dataset.

Abstract

Attention mechanisms are critically important in the advancement of synthetic aperture radar (SAR) automatic target recognition (ATR) systems. Traditional SAR ATR models often struggle with the noisy nature of the SAR data, frequently learning from background noise rather than the most relevant image features. Attention mechanisms address this limitation by focusing on crucial image components, such as the shadows and small parts of a vehicle, which are crucial for accurate target classification. By dynamically prioritizing these significant features, attention-based models can efficiently characterize the entire image with a few pixels, thus enhancing recognition performance. This capability allows for the discrimination of targets from background clutter, leading to more practical and robust SAR ATR models. We show that attention modules increase top-1 accuracy, improve input robustness, and are qualitatively more explainable on the MSTAR dataset.

Figures (2)

• Figure 1: Original Image vs. Grad-CAM gradcam CBAM Attention Model squeezeexcitation with a ResNet-18 resnet Backbone vs. ResNet-18
• Figure 2: Grad-CAM gradcam activation saliency map from each model with a ResNet backbone on the MSTAR dataset