Collaborative Learning of Scattering and Deep Features for SAR Target Recognition with Noisy Labels

TL;DR

This paper introduces a novel collaborative learning framework that fuses scattering and deep features for SAR target recognition, effectively handling noisy labels and improving robustness in challenging conditions.

Contribution

It proposes a multi-model feature fusion framework with dynamic graph modeling and semi-supervised learning to enhance SAR ATR with noisy labels, a novel approach in this domain.

Findings

Achieves state-of-the-art performance on MSTAR dataset.

Effectively handles various levels of label noise.

Improves robustness of SAR ATR under real-world conditions.

Abstract

The acquisition of high-quality labeled synthetic aperture radar (SAR) data is challenging due to the demanding requirement for expert knowledge. Consequently, the presence of unreliable noisy labels is unavoidable, which results in performance degradation of SAR automatic target recognition (ATR). Existing research on learning with noisy labels mainly focuses on image data. However, the non-intuitive visual characteristics of SAR data are insufficient to achieve noise-robust learning. To address this problem, we propose collaborative learning of scattering and deep features (CLSDF) for SAR ATR with noisy labels. Specifically, a multi-model feature fusion framework is designed to integrate scattering and deep features. The attributed scattering centers (ASCs) are treated as dynamic graph structure data, and the extracted physical characteristics effectively enrich the representation of deep image features. Then, the samples with clean and noisy labels are divided by modeling the loss distribution with multiple class-wise Gaussian Mixture Models (GMMs). Afterward, the semi-supervised learning of two divergent branches is conducted based on the data divided by each other. Moreover, a joint distribution alignment strategy is introduced to enhance the reliability of co-guessed labels. Extensive experiments have been done on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, and the results show that the proposed method can achieve state-of-the-art performance under different operating conditions with various label noises.