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.

在合成孔径雷达（SAR）等风险高、成本高的场景中，可解释的人工智能（XAI）对于提高深度神经网络的透明度和可信度至关重要。Shapley 是一种具有强大数学基础的基于博弈的解释技术。然而，Shapley 假设模型的特征是独立的，这使得高维模型的 Shapley 解释无效。本研究通过将高维特征投影到低维流形特征，并随后获得 Fusion-Shap，引入了一种基于流形的 Shapley 方法，旨在（1）解决传统 Shap 遇到的错误解释问题；（2）解决传统 Shap 在复杂场景中面临的可解释性挑战。