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

Semisupervised 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 pseudolabeling with low confidence, which weakens the effect of semisupervised few-shot classification. To solve these issues, a hypergraph matching network is proposed for the semisupervised 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 pseudolabels with high confidence. 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.