Max-Mahalanobis Centers Guided Adversarial Network for Generalized Few-Shot Radar Target Recognition

Abstract

Deep neural network (DNN)-based synthetic aperture radar (SAR) automatic target recognition (ATR) methods have been shown to be vulnerable, particularly in low-data scenarios, also known as generalized few-shot learning situations. This vulnerability significantly restricts their real-world applications. Therefore, enhancing the generalization capability of DNN-based methods is crucial for practical SAR target recognition. To address this challenge, we observe that misclassified samples in generalized few-shot SAR target recognition often exhibit similar behaviors to adversarial samples–they tend to be located near the classification boundary and are distant from their respective class centers. To tackle this issue, we propose a max-Mahalanobis centers (MMC)-guided adversarial network (M2cgAN). This approach focuses on learning compact representations that cluster around preset optimal centers for different targets, thereby reducing the likelihood of misclassifying SAR images. Specifically, we first utilize MMC due to its property that if the input is distributed according to a max-Mahalanobis distribution, a linear classifier will achieve optimal robustness against misclassified samples. Building on this, we leverage this property in generalized few-shot SAR ATR by employing a DNN, specifically ResNet12, to project SAR images onto the preset optimal centers defined by MMC. Finally, an adaptive area perception module has been developed to extract the most discriminative regions in SAR images, effectively reducing the impact of strong scattering points in complex backgrounds. The effectiveness and efficiency of M2cgAN are validated through comprehensive experiments on the widely used MSTAR and OpenSARShip benchmarks.