Abstract Attention mechanism in deep learning is similar to information selection mechanism, and the goal of attention is to select critical information for the current task. In hyperspectral classification, the distinction of some categories depends on the subtle differences, however, traditional classification methods are insufficient to discriminate the fine differences of categories. In this paper, a classification method based on group attention is proposed to enhance the difference of hyperspectral data between categories. Firstly, we slice the hyperspectral sample into several groups on spectral channels, and extract the group convolutional neural networks (CNN) features. Then we use the attention module to obtain the attention weights for each spectral group. Finally, the “feature recalibration” strategy is used to recalibrate the spectral group CNN features. The experiment shows that the proposed approach can improve the classification accuracy of categories with subtle differences.

中文翻译：

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用于具有光谱可分离性分析的高光谱图像分类的光谱组注意网络

摘要 深度学习中的注意力机制类似于信息选择机制，注意力的目标是为当前任务选择关键信息。在高光谱分类中，某些类别的区分取决于细微的差异，而传统的分类方法不足以区分类别的细微差异。本文提出了一种基于群体注意力的分类方法，以增强高光谱数据在类别之间的差异。首先，我们在光谱通道上将高光谱样本分成几组，并提取组卷积神经网络（CNN）特征。然后我们使用注意力模块获得每个谱组的注意力权重。最后，“特征重新校准”策略用于重新校准光谱组CNN特征。实验表明，所提出的方法可以提高具有细微差别的类别的分类准确率。