Hyperspectral image classification based on parallel-branch expectation-maximization attention mechanism

Hyperspectral images (HSI) cover a very large area, how to achieve excellent classification performance with limited time consumptions is still a challenging issue. To reduce running time and improve accuracy, a parallel-branch expectation-maximization (PBEM) attention principle method will be proposed to HSIs classification in this article. In my cognition, this may be the first study to apply the expectation-maximization attention methodology in hyperspectral image classification. Besides, we believe we are the first to combine the disout layer and the expectation-maximization attention methodology in hyperspectral image classification. The experimental results from benchmark dataset prove the superiority of our team proposed methodology in hyperspectral image classification, especially in small sample classification task.