Uncertainty-informed SAR image classification with Bayesian neural networks

Tian Ye; Rajgopal Kannan; Viktor Prasanna; Xu Wang; Carl Busart

doi:10.1117/12.3013963

7 June 2024 Uncertainty-informed SAR image classification with Bayesian neural networks

Tian Ye, Rajgopal Kannan, Viktor Prasanna, Xu Wang, Carl Busart

Proceedings Volume 13039, Automatic Target Recognition XXXIV; 130390I (2024) https://doi.org/10.1117/12.3013963
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tian Ye, Rajgopal Kannan, Viktor Prasanna, Xu Wang, and Carl Busart "Uncertainty-informed SAR image classification with Bayesian neural networks", Proc. SPIE 13039, Automatic Target Recognition XXXIV, 130390I (7 June 2024); https://doi.org/10.1117/12.3013963

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available