Hyperspectral Image Classification Based on Double-Hop Graph Attention Multiview Fusion Network

Abstract

Hyperspectral image (HSI) is pivotal in ground object classification, owing to its rich spatial and spectral information. Recently, convolutional neural networks and graph neural networks have become hotspots in HSI classification. Although various methods have been developed, the problem of detail loss may still exist when extracting complex features within homogenous regions. To solve this issue, in this article, we proposed a double-hop graph attention multiview fusion network. This model is adept at pinpointing precise attention features by integrating a double-hop graph with the graph attention network, thereby enhancing the aggregation of multilevel node information and surmounting the limitations of a restricted receptive field. Furthermore, the spectral-coordinate attention module (SCAM) is presented to seize more nuanced spectral and spatial attention features. SCAM harnesses the coordinate attention mechanism for in-depth pixel-level global spectral–spatial view. Coupled with the multiscale Gabor texture view, we forge a multiview fusion network that meticulously highlights edge details across varying scales and captures beneficial features. Our experimental validation across four renowned benchmark HSI datasets showcases our model's superiority, outstripping comparative methods in classification accuracy with limited labeled samples.