Revisiting Local and Global Descriptor-Based Metric Network for Few-Shot SAR Target Classification

Convolutional Neural Network (CNN) still suffers from overfitting problems caused by limited samples in SAR target classification. Few-shot learning (FSL) aims to learn a classifier to classify images when only a few training examples are available for each class. Recent works on FSL demonstrate that using local descriptor representations can lead to more informative representations than using image-level representations. However, local descriptors typically capture only local information in an image, while disregarding global contextual information. Therefore, relying solely on local descriptors may not fully represent the features of an image, thus affecting the classification performance of the SAR targets. To address this problem, we propose a novel global and local descriptor-based metric network (GLMnet) for few-shot SAR target classification. The proposed GLMnet consists of global-level metric relationships and local-level metric relationships. The global-level metric relationships are computed by the Brownian distance covariance metric module to capture the relationships between image blocks. On the other hand, the local-level metric relationships are generated by the image-to-class metric module. To integrate these two metric relationships, we design an adaptive metric fusion module to achieve a more discriminative measurement performance. Furthermore, to address the issue of prototypes deviating from the true distribution due to the presence of outliers or noisy samples in the BDC metric process, we design a weighted average prototype computation module to obtain reliable prototypes. Experimental results on three SAR datasets show that the proposed GLMnet can generate reliable prototypes and achieve comparable classification results in contrast to other FSL algorithms.