Discovering and Explaining the Non-Causality of Deep Learning in SAR ATR

Synthetic aperture radar automatic target recognition (SAR ATR) is one of the critical technologies for SAR image interpretation, which has an important application prospect in military and civilian fields. Deep learning has been widely used in this area and achieved an excellent recognition rate on the benchmark dataset in recent years. However, the benchmark dataset suffers from data selection bias due to a single data collection condition. This data bias enhances deep learning models to overfit non-causal background clutter. Moreover, existing methods qualitatively analyze the model causality and do not deeply analyze this data bias. In this paper, we explicitly show that the data selection bias leads to the non-causality of the model and spurious correlation of clutter. First, we quantify the contribution of the target, clutter, and shadow regions during the training process through the Shapley value. The clutter contribution has a large proportion during the training process. Second, the causes of the non-causality of deep learning in SAR ATR include data selection bias and model texture bias. Data selection bias results in class-related clutter and false feature representation. Furthermore, the spurious correlation of clutter arises from the similar signal-to-clutter ratios (SCR) between the training and test sets. Finally, we propose a random SCR re-weighting method to reduce the overfitting for clutter. However, the model texture bias increases with model complexity after removing data bias. The experimental results of different models under the standard operating condition of the benchmark MSTAR dataset prove the above conclusions.