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，深度学习被广泛应用于SAR ATR，并在MSTAR数据集上取得了出色的性能。然而，由于成像条件的限制，MSTAR存在数据偏差，如背景相关性，即背景杂波属性与目标类之间存在一定程度的虚假相关性。深度学习可以过度拟合杂波，以减少训练误差。因此，对于杂波来说，过拟合的程度反映了深度学习在SAR ATR中的非因果性。现有方法只是定性地分析这种现象。在本文中，我们根据Shapley值量化了不同区域对目标识别的贡献。杂波的Shapley值度量过拟合的程度。此外，我们解释了数据偏差和模型偏差对非因果性的贡献。简而言之，数据偏差导致训练集和测试集具有可比性的信杂比和杂波纹理。不同的模型结构对这些偏差的过拟合程度不同。在MSTAR数据集上的标准工作条件下，各种模型的实验结果支持了我们的结论。我们的代码可在https://github.com/waterdisappear/Data-Bias-in-MSTAR 上获取。