Simulated Data Feature Guided Evolution and Distillation for Incremental SAR ATR

Deep neural network (DNN)-based synthetic aperture radar automatic target recognition (SAR ATR) methods have made great progress in recent years. However, the performance of DNN models relies on a large number of independent and identically distributed measured synthetic aperture radar (SAR) images, which is contrary to the SAR ATR in practice. Furthermore, DNN models also suffer from catastrophic forgetting when learning a sequence of new classes. To tackle these problems, we introduce simulated data into the class incremental learning of SAR ATR for the first time. Specifically, we aim to continuously learn a sequence of new classes with a small amount of measured data and a large amount of simulated data. We first investigate the properties of incremental learning using simulated data, and the main observation is that simulated data can achieve good performance in short-term incremental learning rather than long-term incremental learning. A novel class incremental learning method, namely, feature guided evolution and distillation (FGED), is then presented. On the one hand, FGED encourages simulated data to have the same feature relationship structure as the corresponding measured data to reduce their distribution discrepancy in short-term incremental learning. On the other hand, FGED adopts a feature distillation strategy to simultaneously reduce the distribution discrepancy accumulation of previous incremental classes and alleviate the catastrophic forgetting in long-term incremental learning. The experimental results obtained on the MSTAR benchmark dataset and two simulated datasets demonstrate the effectiveness of FGED.