Hierarchical attention networks for hyperspectral image classification

Attention can be interpreted as a method which allocates available computing power to the most informative part of the signal. In deep learning, attention mechanism also helps us to dig out the subtle information. In hyperspectral classification, the discrimination of some land cover types depends on the fine differences of hyperspectral, but most classification methods do not focus on the fine differences between hyperspectral categories. In this paper, a hierarchical group attention classification method is proposed to focus on the differences of categories from coarse to fine, therefore, the fine differences between categories can be obtained to achieve more accurate classification. For comparison and validation, we test the proposed approach with three other classification approaches on Salinas and Indian datasets, and the experiments demonstrate that our proposed approach can distinguish the spectral subtle differences of similar categories more accurately.