To fully use the contextual information of hyperspectral images (HSIs), we propose a U-shaped network model combined with attention mechanism to achieve image-level HSI classification. First, the entire HSI is input into the network for end-to-end training, and the classification results of the entire scene are directly output. Then, the context information is used to improve the classification accuracy, while reducing many redundant calculations. Second, to improve the classification accuracy, considering two dimensions (i.e., space and channel), a hybrid attention module, mixing spatial and channel, is designed. Third, three datasets of the University of Pavia, Indian Pines, and Salinas are selected for the classification experiments. The experimental results show that, compared with other methods, the proposed method can obtain higher classification accuracy, and its training and testing efficiency is higher.

中文翻译：

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用于高光谱图像分类的注意力 U 形网络

为了充分利用高光谱图像（HSI）的上下文信息，我们提出了一种结合注意力机制的U形网络模型来实现图像级的HSI分类。首先将整个HSI输入网络进行端到端的训练，直接输出整个场景的分类结果。然后，使用上下文信息来提高分类精度，同时减少许多冗余计算。其次，为了提高分类精度，考虑两个维度（即空间和通道），设计了混合注意力模块，混合空间和通道。第三，选择帕维亚大学、印度松树和萨利纳斯大学的三个数据集进行分类实验。实验结果表明，与其他方法相比，