Random Interpolation Data Augmentation for Incremental Automatic Target Recognition
Bin Li, Zongyong Cui, Yijie Deng, Zheng Ou Zhou, Zongjie Cao · 2024
Traditional supervised learning methods have achieved great success in automatic target recognition (ATR). Unfortunately, if the model is trained exclusively on new class samples, the model will forget all knowledge about the old class samples. This phenomenon is called catastrophic forgetting. Incremental learning method can prevent catastrophic forgetting by keeping a small number of old class samples as exemplars and training them together with new class samples. However, the ratio of old to new class samples is still seriously out of balance. In this paper, an data augmentation method of old class samples is proposed by using random interpolation (RI) between two exemplars to generate fake samples in the process of incremental learning. In this method, the distribution of the original classes is partially restored while also creating a numerical balance between the old and new class samples. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset demonstrate the effectiveness of this method in Incremental SAR ATR.