Uncertainty-informed SAR image classification with Bayesian neural networks

Abstract

This paper summarizes our work in alleviating the vulnerability of neural networks for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) to adversarial perturbations. We propose an approach of robust SAR image classification that integrates Bayesian Neural Networks (BNNs) to harness epistemic uncertainty for distinguishing between clean and adversarially manipulated SAR images. Additionally, we introduce a visual explanation method that employs a probabilistic variant of Guided Backpropagation (GBP) specifically adapted for BNNs. This method generates saliency maps highlighting critical pixels, thereby aiding human decisionmakers in identifying adversarial scatterers within SAR imagery. Our experiments demonstrate the effectiveness of our approach in maintaining high True Positive Rates (TPR) while limiting False Positive Rates (FPR), and in accurately identifying adversarial scatterers, showcasing our method's potential to enhance the reliability and interpretability of SAR ATR systems in the face of adversarial threats. Details of our method and experiments can be found in Ref. 1.