A Causality-Erasing Baseline Values of SHAP Method for Enhancing Explainable Deep Networks in SAR Target Classification

The extensive adoption of deep learning techniques in SAR image interpretation has spurred a heightened interest in the explainability of these models. SHAP, a masking-based explainability method, relies on conventional occlusion techniques (baseline values) that are frequently overly rigid and introduce varying degrees of interference. To faithfully capture the influence of distinct regions in SAR images on the overall interpretation, we introduce a baseline value algorithm based on multivariate interactions and SAR clutter distribution. This algorithm is applied to SHAP attribution. Experimental results on the MSTAR dataset illustrate that the baseline values computed by our algorithm effectively eliminate all existing significant patterns in the original samples without introducing new patterns.