End-to-End Multilevel Hybrid Attention Framework for Hyperspectral Image Classification
Jianhong Xiang, Wei Chen, Minhui Wang, Teng Long
Abstract
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.