Unsupervised Multiview Graph Contrastive Feature Learning for Hyperspectral Image Classification

As a popular deep learning (DL) algorithm, graph neural network (GNN) has been widely used in hyperspectral image (HIS) classification. However, most of the GNN-based classification algorithms are concentrated in the field of semisupervision, which heavily relies on the quantity and quality of samples. To solve this problem, we propose an unsupervised multiview graph contrastive (UMGC) feature learning algorithm to explore the deep semantic features of HSIs without being constrained by samples. First, we construct multiview adjacency matrixes from spatial and spectral directions. Second, the adaptive data augmentation method is used to selectively enhance the topology and attribute structure of the graph. Thereafter, features are extracted by using a contrastive loss to maximize the similarity between the two views. Finally, we tested the model's performance based on multiple evaluation methods. Experimental results on three publicly available hyperspectral datasets show that the proposed UMGC can have better classification performance compared with other state-of-the-art unsupervised feature extraction (FE) methods.