Hyperspectral Image Classification Based on Multi-Level Spectral-Spatial Transformer Network
Hao Yang, Haoyang Yu, Danfeng Hong, Zhen Xu, Yulei Wang, Meiping Song
Abstract
Deep learning methods have been widely used in hyperspectral image classification (HSIC). In recent years, Convolutional Neural Network (CNN) has become a mainstream model of deep learning for HSIC. Although the CNN-based method has made great progress, it still faces a series of challenges such as insufficient use of long-distance information, limited receiving domain, and high computational overhead. In order to overcome these issues, this paper proposes a multi-level spectral-spatial transformer network (MSTNet) for HSIC through the image-based classification framework. The proposed network learns feature representation through a transformer encoder, and integrates multi-level features through a decoder to generate classification results. Finally, the experimental results on two real hyperspectral data sets verified the superiority of the method.