  Explainable artificial intelligence (XAI) holds immense significance in
enhancing the deep neural network's transparency and credibility, particularly
in some risky and high-cost scenarios, like synthetic aperture radar (SAR).
Shapley is a game-based explanation technique with robust mathematical
foundations. However, Shapley assumes that model's features are independent,
rendering Shapley explanation invalid for high dimensional models. This study
introduces a manifold-based Shapley method by projecting high-dimensional
features into low-dimensional manifold features and subsequently obtaining
Fusion-Shap, which aims at (1) addressing the issue of erroneous explanations
encountered by traditional Shap; (2) resolving the challenge of
interpretability that traditional Shap faces in complex scenarios.
