Background Debiased SAR Target Recognition via Causal Interventional
  Regularizer
Hongwei Dong Fangzhou Han Lingyu Si Wenwen Qiang Lamei Zhang
Recent studies have utilized deep learning (DL) techniques to automatically
extract features from synthetic aperture radar (SAR) images, which shows great
promise for enhancing the performance of SAR automatic target recognition
(ATR). However, our research reveals a previously overlooked issue: SAR images
to be recognized include not only the foreground (i.e., the target), but also a
certain size of the background area. When a DL-model is trained exclusively on
foreground data, its recognition performance is significantly superior to a
model trained on original data that includes both foreground and background.
This suggests that the presence of background impedes the ability of the
DL-model to learn additional semantic information about the target. To address
this issue, we construct a structural causal model (SCM) that incorporates the
background as a confounder. Based on the constructed SCM, we propose a causal
intervention based regularization method to eliminate the negative impact of
background on feature semantic learning and achieve background debiased
SAR-ATR. The proposed causal interventional regularizer can be integrated into
any existing DL-based SAR-ATR models to mitigate the impact of background
interference on the feature extraction and recognition accuracy. Experimental
results on the Moving and Stationary Target Acquisition and Recognition (MSTAR)
dataset indicate that the proposed method can enhance the efficiency of
existing DL-based methods in a plug-and-play manner.