Attention Graph Convolutional Network for Disjoint Hyperspectral Image Classification

Convolutional neural networks (CNNs) are used extensively in remote sensing due to their capacity to capture intricate features from a broad range of object patterns, irrespective of object size, shape, or color. These networks excel at extracting high-frequency spectral information such as angles, edges, and outlines. The classification boundary zone, however, becomes hazy for CNNs because they learn characteristics by means of a fixed shape kernel concentrated on the central pixel and can perform poorly in image classification at class boundaries. In addition, CNNs are not designed to capture global relationships. Thus, in this letter, we propose an attention graph convolutional network (Attention-GCN) as a solution to the aforementioned shortcomings. The developed model illustrated a high level of superiority over several CNN and vision transformer (ViT)-based models. For example, in the Augsburg data benchmark, the developed algorithm exhibited an average accuracy of 61.11%, substantially outperforming other models such as HybridSN, iFormer, EfficientFormer, graph convolutional network (GCN), CoAtNet, 2D-CNN, 3D-CNN, and ResNet by approximately 9, 13, 14, 15, 18, 24, 25, and 29 percentage points, respectively. The code will be made publicly available at https://github.com/aj1365/AGCN