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）中广泛应用，并在MSTAR数据集上取得了优异性能。然而，由于成像条件受限，MSTAR数据集存在背景相关性等数据偏差，即背景杂波属性与目标类别存在虚假关联。深度学习可通过过拟合杂波来降低训练误差，因此杂波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。本文基于沙普利值量化不同区域对目标识别的贡献，其中杂波的沙普利值可衡量过拟合程度。此外，我们进一步解释了数据偏差与模型偏差如何共同导致非因果性：简而言之，数据偏差使得训练集与测试集的信号杂波比及杂波纹理具有可比性，而不同模型结构对这些偏差存在不同程度的过拟合。MSTAR数据集上多种模型在标准操作条件下的实验结果支持了我们的结论。代码详见https://github.com/waterdisappear/Data-Bias-in-MSTAR。