Long-Tailed SAR Target Recognition Based on Expert Network and Intraclass Resampling
YingBing Liu, Fan Zhang, Lixiang Ma, Fei Ma
Abstract
In recent years, it has been a research hotspot to apply big data-driven deep learning methods to Synthetic Aperture Radar (SAR) target recognition with limited data. However, the problem caused by the long-tailed characteristics of SAR data has long been ignored. Specifically, a majority of data samples are concentrated in a few categories, leading to a skewed distribution of data. This skewed distribution can cause learning bias towards the majority class, which can subsequently degrade the recognition performance of the minority class. This issue is further exacerbated in limited sample conditions for SAR target recognition. After conducting research on target recognition for long-tailed natural images, this study has found that the existing methods used in this field cannot be easily applied to SAR target recognition. The primary reason is that SAR image data exhibit simultaneous and complex inter-class and intra-class long-tailed distributions. In response to this issue, we proposes the use of a multi-branch expert network and dual-environment sampling to address the long-tail problems in both inter-class and intra-class scenarios. The proposed method outperforms popular long-tailed target recognition methods on the long-tailed versions of the MSTAR and FUSAR datasets.