S2GFormer: Exploring Relationship Between Transformer and Graph Convolution for Hyperspectral Image Classification
摘要
Recently both graph convolutional neural networks (GCNs) and Transformers have shown promising progress in hyperspectral image (HSI) classification. Transformer-based methods have a great ability to model non-local interactions among spectral and spatial information, whereas GCNs tend to do well in exploiting neighborhood vertex interactions based on their unique aggregation mechanism.