In recent years, deep learning has been widely used in SAR ATR and achieved excellent performance on the MSTAR dataset. However, due to constrained imaging conditions, MSTAR has data biases such as background correlation, i.e., background clutter properties have a spurious correlation with target classes. Deep learning can overfit clutter to reduce training errors. Therefore, the degree of overfitting for clutter reflects the non-causality of deep learning in SAR ATR. Existing methods only qualitatively analyze this phenomenon. In this paper, we quantify the contributions of different regions to target recognition based on the Shapley value. The Shapley value of clutter measures the degree of overfitting. Moreover, we explain how data bias and model bias contribute to non-causality. Concisely, data bias leads to comparable signal-to-clutter ratios and clutter textures in training and test sets. And various model structures have different degrees of overfitting for these biases. The experimental results of various models under standard operating conditions on the MSTAR dataset support our conclusions. Our code is available at https://github.com/waterdisappear/Data-Bias-in-MSTAR.

翻译：近年来，深度学习在SAR自动目标识别（SAR ATR）领域得到广泛应用，并在MSTAR数据集上取得了优异性能。然而，由于成像条件的限制，MSTAR数据集存在诸如背景相关性等数据偏差，即背景杂波属性与目标类别间存在虚假相关。深度学习可能过度拟合杂波以减少训练误差。因此，对杂波的过拟合程度反映了深度学习在SAR ATR中的非因果性。现有方法仅能定性分析这一现象。本文基于沙普利值（Shapley value）量化不同区域对目标识别的贡献，其中杂波的沙普利值衡量了过拟合程度。此外，我们解释了数据偏差与模型偏差如何共同导致非因果性：简言之，数据偏差导致训练集与测试集中信号杂波比及杂波纹理存在相似性；而不同模型结构对此类偏差的过拟合程度各异。基于MSTAR数据集在标准操作条件下的多种模型实验结果支持了我们的结论。代码已开源：https://github.com/waterdisappear/Data-Bias-in-MSTAR。