ABSTRACT

In this study, we introduce a non-local block of the attention mechanism into capsule neural network (CapsNet) to form a non-local capsule network (NLCapsNet) for hyperspectral remote sensing image (HSI) classification. The presented NLCapsNet uses global information from input images and has a powerful representation of the capacity and spatial relationships among HSI features. It can effectively isolate invalid information and consolidate valid information, in addition to learning more representative features and capturing the long-distance dependencies of HSIs with only a few layers. An additional convolutional layer is embedded before the capsule layers to capture high-level features and speed up the routing procedure. The proposed method can effectively enhance the classification accuracy with a rapid convergence speed and avoid overfitting when the number of training samples is limited. The NLCapsNet performs well on the classification of the Kennedy Space Center, Pavia University and Salinas datasets.

摘要

在这项研究中，我们将注意机制的非本地块引入到胶囊神经网络（CapsNet）中，以形成用于高光谱遥感图像（HSI）分类的非本地胶囊网络（NLCapsNet）。所展示的NLCapsNet使用来自输入图像的全局信息，并具有HSI功能之间的容量和空间关系的强大表示。除了学习更多具有代表性的功能并捕获仅几层的HSI的远距离依赖关系外，它还可以有效地隔离无效信息并合并有效信息。在胶囊层之前嵌入了一个附加的卷积层，以捕获高级特征并加快路由过程。所提出的方法能够以快速的收敛速度有效地提高分类精度，并在训练样本数量有限时避免过拟合。NLCapsNet在肯尼迪航天中心，帕维亚大学和萨利纳斯数据集的分类方面表现良好。