ASC-RISE: Physical Information Guided Explanation of SAR ATR Models

Deep learning models have shown excellent performance in synthetic aperture radar (SAR) automatic target recognition (ATR) tasks. However, the opacity of the decision-making mechanisms within these models hamper their credibility in practical applications. Therefore, numerous explainable artificial intelligence (XAI) methods have been developed to interpret the models. Among them, the randomized input sampling for explanation (RISE) method introduces input random pixel perturbations to observe resulting changes in the output by generating saliency heatmaps. However, unlike optical images, SAR images have their unique physical properties. This paper introduces the attributed scattering center (ASC) into the RISE method, known as the ASC-RISE method guided by physical information, to explain the model. Experimental results demonstrate that the heatmaps generated by ASC-RISE effectively locate the model’s decision features and provide corresponding physical information.