Simulated Data-Guided Incremental SAR ATR Through Feature Aggregation

Abstract

Applying synthetic aperture radar automatic target recognition (SAR ATR) in open scenario based on deep learning (DL) is challenging due to the difficulty in incrementally recognizing new targets with limited samples. To address this challenge, we introduce simulated data that reflects the structure and scattering features of the new target to supplement measured data for better performance, and then, we propose a novel class incremental SAR ATR method guided by simulated data through feature aggregation (SGFA). Due to the gap between simulated and measured data, DL-model prefers extracting simulated-specific features in incremental learning, resulting in misclassification of new targets. In order to avoid the bias learning of simulated data, SGFA utilizes feature aggregation to extract scattering and structural features that are present in both simulated and measured images, which consists of measured data-anchored minibatch construction strategy (MDA) and feature-level contrastive loss. Specifically, the MDA can reduce the high sampling probability of a large number of simulated samples in each minibatch. The feature-level contrastive loss can aggregate the feature distributions of simulated and measured data, which is obtained by automatically constructing sample pairs through cyclic shifts of feature vectors in the minibatch. In addition, a small amount portion of simulated data is retained to resist severe forgetting caused by the difficulty of adequately representing the data distribution with limited measured data. The experiments on SAMPLE dataset demonstrate the effectiveness of the proposed method.

Keywords

• Automatic target recognition (ATR)
• class incremental learning
• simulation
• synthetic aperture radar (SAR)