Exploring the Explainability of SAR Target Classification Using Shap Method with Different Baseline Values

Deep Learning (DL) models have proven effective in Synthetic Aperture Radar (SAR) images classification. However, the black-box nature of deep learning models hinders explainability. A common approach to compute the attributes of input variables in a sample involves masking some input variables of a deep neural network (DNN) and measuring the output variation of the masked input sample. Typically, baseline values of input variables are used to mask the input variables. Recent research suggests that the effectiveness of masking methods with different baseline values. In this paper, we compare Zero, Mean, and Random baseline values based on the SHAP method and provide guidance for practitioners in selecting an appropriate masking method for similar tasks.