Hyperspectral Image Classification Based on 3D–2D Hybrid Convolution and Graph Attention Mechanism

Abstract Convolutional neural networks and graph convolutional neural networks are two classical deep learning models that have been widely used in hyperspectral image classification tasks with remarkable achievements. However, hyperspectral image classification models based on graph convolutional neural networks using only shallow spectral or spatial features are insufficient to provide reliable similarity measures for constructing graph structures, limiting their classification performance. To address this problem, we propose a new end-to-end hyperspectral image classification model combining 3D–2D hybrid convolution and a graph attention mechanism (3D–2D-GAT). The model utilizes the collaborative work of hybrid convolutional feature extraction module and GAT module to improve classification accuracy. First, a 3D–2D hybrid convolutional network is constructed and used to quickly extract the discriminant deep spatial-spectral features of various ground objects in hyperspectral image. Then, the graph is built based on deep spatial-spectral features to enhance the feature representation ability. Finally, a network of graph attention mechanism is adopted to learn long-range spatial relationship and distinguish the intra-class variation and inter-class similarity among different samples. The experimental results on three datasets, Indian Pine, the University of Pavia and Salinas Valley show that the proposed method can achieve higher classification accuracy compared with other advanced methods.