SVD-Based Feature Reconstruction Metric Network With Active Contrast Loss for Few-Shot SAR Target Recognition

Synthetic aperture radar (SAR) automatic target recognition (ATR) methods based on convolutional neural networks require a large number of samples to achieve good generalize. However, in practical SAR applications, the scarcity of samples has always been a persistent issue, especially in the military domain, where certain target types may have very few samples. This scarcity makes SAR target feature extraction more difficult, posing a challenge to SAR ATR. To address this issue, we propose an SVD-based feature reconstruction metric network with active contrast loss for few-shot SAR target recognition. First, a self-supervised despeckling module is proposed, which applies a logarithmic transformation to SAR images, converting multiplicative speckle noise into additive noise, and then uses a self-supervised denoising network to suppress Gaussian noise. Finally, the image is restored through an exponential inverse transformation, achieving effective denoising of SAR images. Second, an SVD-based feature reconstruction module is proposed, which performs singular value decomposition on the extracted features and retains the key singular values for reconstruction. By extracting the main structural information, noise and unimportant details are suppressed, thereby improving classification accuracy. Furthermore, an active contrastive loss is proposed to reduce the distance between features of the same class and increase the separability between features of different classes by selecting representative samples to establish positive-negative sample pairs. Experimental results demonstrate that our proposed algorithm can achieve comparable metric performance compared to other metric-based few-shot learning methods.