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，深度学习已广泛应用于SAR自动目标识别，并在MSTAR数据集上取得了优异性能。然而，受限于有限的成像条件，MSTAR存在数据偏差，例如背景相关性，即背景杂波特性与目标类别之间存在虚假关联。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自动目标识别中的非因果性。现有方法仅对该现象进行定性分析。本文基于Shapley值量化不同区域对目标识别的贡献，其中杂波的Shapley值衡量过拟合程度。此外，我们解释了数据偏差与模型偏差如何导致非因果性：简言之，数据偏差使训练集与测试集中的信号-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的过拟合程度各异。针对MSTAR数据集在标准操作条件下的多种模型实验结果支持了上述结论。我们的代码已开源至https://github.com/waterdisappear/Data-Bias-in-MSTAR。