Graph-infused hybrid vision transformer: Advancing GeoAI for enhanced land cover classification

Abstract

Hyperspectral Image Classification (HSIC) is a challenging task due to the high-dimensional nature of Hyperspectral Imaging (HSI) data and the complex relationships between spectral and spatial information. This paper proposes a Graph-Infused Hybrid spatial–spectral Transformer (GFormer) for HSIC. The GFormer combines the power of graph and spatial–spectral transformer to capture both spectral relationships and spatial context. We represent the HSI data as a graph, where nodes represent pixels and edges capture spectral similarities. By incorporating an attention mechanism, the GFormer learns spatial–spectral fusion representations, allowing it to effectively discriminate between different classes. The model can capture long-range dependencies among spectral bands, enabling it to understand complex interactions in the HSI data. Moreover, the GFormer adapts to different spectral resolutions by dynamically adjusting attention weights for each spectral band. Experimental results on benchmark HSI datasets demonstrate that the GFormer outperforms state-of-the-art (SOTA) methods, achieving superior classification accuracy.

Keywords

• Hyperspectral image classification
• Spatial–spectral transformer
• Land cover classification
• Vision transformers
• Graph attention transformers