End-to-End Multilevel Hybrid Attention Framework for Hyperspectral Image Classification

HSI has abundant spectral–spatial information. Using this information to improve the accuracy of HSI classification is a hot issue in the industry. This letter proposes an end-to-end multilevel hybrid attention network (DMCN). It is composed of a dense 3-D convolutional neural network (3D-CNN), grouped residual 2D-CNN, and coordinate attention that can perceive categories. In the case of a small number of training samples, DMCN can still extract spectral–spatial fusion information and learn spatial features more deeply for classification. Experiments are conducted on three well-known hyperspectral datasets, i.e., Indian Pines (IP), University of Pavia (UP), and Salinas (SA). The results show that DMCN achieved 92.39%, 97.28%, and 98.40% classification accuracy in IP, UP, and SA.