Hypergraph Matching Network for Semi-Supervised Few-Shot Scene Classification of Remote Sensing Images

Semi-supervised few-shot learning aims to alleviate the issue of insufficient labeled data with additional unlabeled samples. As for remote sensing images, complex contextual information leads to pseudo labeling with low confidence, which weakens the effect of semi-supervised few-shot classification. To solve these issues, a hypergraph matching network is proposed for semi-supervised few-shot scene classification of remote sensing images. Specifically, a hypergraph propagation module is designed to construct a hypergraph network, which can take advantage of adjacent samples with similar semantics and improve the representation ability of class prototypes. Then, a cross-layer prototype matching module is proposed to dynamically match features of different scales and angles, which aims to predict pseudo labels with high confidences. Experimental results on three public remote sensing datasets demonstrate that the proposed method can make effective utilization of additional unlabeled samples to enhance the classification performance of few-shot learning.