Cross-Domain Few-Shot Learning Based on Graph Convolution Contrast for Hyperspectral Image Classification

Training a deep-learning classifier notoriously requires hundreds of labeled samples at least. Many practical hyperspectral image (HSI) scenarios suffer from a substantial cost associated with obtaining a number of labeled samples. Few-shot learning (FSL), which can realize accurate classification with prior knowledge and limited supervisory experience, has demonstrated superior performance in the HSI classification. However, previous few-shot classification algorithms assume that the training and testing data are distributed in the same domains, which is a stringent assumption in realistic applications. To alleviate this limitation, we propose a cross-domain FSL based on graph convolution contrast (GCC-FSL). The proposed method leverages cross-domain learning to acquire transferable knowledge from the source domain for classifying samples in the target domain. Specifically, a positive and negative pairs module is designed for constructing positive and negative pairs by matching the class prototypes of the target domain with those of the source domain, which aligns the data distribution of the source and target domains. In addition, a graph convolution contrast (GCC) module is proposed for extracting global graph-structure information of HSI to improve the ability of feature expression and constructing a graph-contrast loss to solve a domain-shift problem. Finally, a multiscale feature extraction network is designed to expand convolutional receptive fields through feature reuse and increase information interaction for fine-grained feature extraction. The experimental results demonstrate the improved performance for the proposed FSL framework relative to both state-of-the-art convolutional neural network (CNN)-based methods as well as other few-shot techniques. The source code of this method can be found at https://github.com/JieW-ww/GCC-FSL.