Hierarchical Attention Transformer for Hyperspectral Image Classification
Tahir Arshad, Junping Zhang
Abstract
Hyperspectral image data contains rich spectral-spatial information which can be useful for various applications. Many methods have been proposed to classify the hyperspectral images. Nonetheless, the availability of limited training samples in traditional models frequently weakens their ability to handle the inherent complexity of the task. Deep Learning models has been successfully applied in the field of remote sensing. In this letter, we propose a vision transformer (ViT) based network called hierarchical attention transformer that combines the properties of local representation learning in 3D and 2D CNNs and potent global modeling capabilities in ViT. We leverage the efficiency of window based self-attention. Within each window, there are dedicated tokens that contribute to both local and global representation learning. The overall accuracy of the proposed model achieved 99.70%,99.89%,99.56%,81.75%and 99.59% on five dataset.