Adaptive Cross-Attention-Driven Spatial-Spectral Graph Convolutional Network for Hyperspectral Image Classification

TL;DR

This paper introduces an adaptive cross-attention-driven spatial-spectral graph convolutional network for hyperspectral image classification, effectively integrating spatial and spectral features with attention mechanisms and adaptive graph learning.

Contribution

It proposes a novel ACSS-GCN model that combines spatial and spectral GCNs with a cross-attention fusion module and adaptive graph learning for improved HSI classification.

Findings

Achieves superior classification accuracy on two HSI datasets.

Effectively fuses spatial and spectral features with attention mechanisms.

Adaptive graph learning enhances model performance.

Abstract

Recently, graph convolutional networks (GCNs) have been developed to explore spatial relationship between pixels, achieving better classification performance of hyperspectral images (HSIs). However, these methods fail to sufficiently leverage the relationship between spectral bands in HSI data. As such, we propose an adaptive cross-attention-driven spatial-spectral graph convolutional network (ACSS-GCN), which is composed of a spatial GCN (Sa-GCN) subnetwork, a spectral GCN (Se-GCN) subnetwork, and a graph cross-attention fusion module (GCAFM). Specifically, Sa-GCN and Se-GCN are proposed to extract the spatial and spectral features by modeling correlations between spatial pixels and between spectral bands, respectively. Then, by integrating attention mechanism into information aggregation of graph, the GCAFM, including three parts, i.e., spatial graph attention block, spectral graph attention block, and fusion block, is designed to fuse the spatial and spectral features and suppress noise interference in Sa-GCN and Se-GCN. Moreover, the idea of the adaptive graph is introduced to explore an optimal graph through back propagation during the training process. Experiments on two HSI data sets show that the proposed method achieves better performance than other classification methods.