Deep Fourier-Based Task-Aware Metric Network for Few-Shot SAR Target Classification

The downsampling operations in convolutional neural networks (CNNs) lead to the loss of high-frequency texture features in images, posing challenges for synthetic aperture radar (SAR) automatic target recognition (ATR). To this end, some algorithms introduce the Fourier transform to retain and restore high-frequency information. However, these algorithms are prone to overfitting with a small number of SAR samples. Additionally, the similarity of different SAR target backgrounds further complicates distinguishing between targets and backgrounds during metric learning. To address these issues, a novel few-shot learning (FSL) method for SAR target classification, namely, the deep Fourier-based task-aware metric network, is proposed. First, we propose a frequency-domain feature extraction module. This module uses a fast Fourier transform (FFT) to convert the input image into the frequency domain, and then performs convolution on the amplitude and phase information to extract and enhance features. Then, the inverse FFT and convolution operations are applied to generate the final frequency-domain feature representation. Furthermore, we propose a parameter-free contrast metric module that identifies and reduces background-related features by calculating the similarity between the query sample and both the target and nontarget classes. This module fully considers the context of the entire task support set, effectively improving the model’s recognition accuracy. Finally, to address the issue of increased sensitivity to noise and outliers due to unimportant local descriptors during the metric process, we design a weighted k-nearest neighbor (k-NN) method to enhance the model’s focus on important information, thereby improving classification accuracy. The experimental results on two public SAR datasets demonstrate that our proposed algorithm achieves higher classification accuracy than other FSL algorithms and exhibits discriminative feature representation and metric capability.