IRASNet: Improved Feature-Level Clutter Reduction for Domain Generalized SAR-ATR

TL;DR

IRASNet is a novel framework that enhances domain-generalized SAR-ATR by reducing clutter at the feature level and learning domain-invariant features, leading to improved recognition accuracy across different datasets.

Contribution

The paper introduces a clutter reduction module combined with adversarial learning and positional supervision, enabling effective domain-invariant feature extraction without using measured data during training.

Findings

Achieves state-of-the-art performance on public SAR datasets.

Significantly improves feature-level clutter reduction.

Enhances generalization across different domains.

Abstract

Recently, computer-aided design models and electromagnetic simulations have been used to augment synthetic aperture radar (SAR) data for deep learning. However, an automatic target recognition (ATR) model struggles with domain shift when using synthetic data because the model learns specific clutter patterns present in such data, which disturbs performance when applied to measured data with different clutter distributions. This study proposes a framework particularly designed for domain-generalized SAR-ATR called IRASNet, enabling effective feature-level clutter reduction and domain-invariant feature learning. First, we propose a clutter reduction module (CRM) that maximizes the signal-to-clutter ratio on feature maps. The module reduces the impact of clutter at the feature level while preserving target and shadow information, thereby improving ATR performance. Second, we integrate adversarial learning with CRM to extract clutter-reduced domain-invariant features. The integration bridges the gap between synthetic and measured datasets without requiring measured data during training. Third, we improve feature extraction from target and shadow regions by implementing a positional supervision task using mask ground truth encoding. The improvement enhances the ability of the model to discriminate between classes. Our proposed IRASNet presents new state-of-the-art public SAR datasets utilizing target and shadow information to achieve superior performance across various test conditions. IRASNet not only enhances generalization performance but also significantly improves feature-level clutter reduction, making it a valuable advancement in the field of radar image pattern recognition.