Graph Meta Transfer Network for Heterogeneous Few-Shot Hyperspectral Image Classification

Abstract

Since obtaining labeled hyperspectral images (HSIs) is difficult and time-consuming, the shortage of training samples has always been a challenge for HSI classification. In practical applications, only a few labeled samples are available in the task domain (target domain), while sufficient labeled samples are available in another domain (source domain). At the same time, these two domains are heterogeneous and contain different categories. This scenario makes it difficult to effectively transfer knowledge from the source domain to the target domain. To address this challenge, we propose a novel heterogeneous few-shot learning (FSL) method, namely graph meta transfer network (GMTN). Specifically, the graph sample and aggregate network (GraphSAGE) and meta-learning, which are both inductive learning, are integrated into a unified framework. In this way, the aggregation function is generalized from abundant few-shot tasks for feature extraction on the source and target domains. The spatial importance strategy (SIS) is designed to guide the feature propagation and alleviate the information interference caused by different categories. The neighborhood receptive field spectral attention (RFSA) mechanism is proposed to model the importance of spectral band using the information of the neighborhood pixels, which enables GMTN to pay more attention to bands with discriminative features in both domains. In addition, the node spatial information reset method is proposed to augment samples based on the spatial position relationship of nodes. Furthermore, to alleviate the domain shift in heterogeneous scenarios, the conditional domain adversarial strategy is used to achieve effective meta-knowledge transfer. Experiments show that GMTN outperforms the compared state-of-the-art methods.