Recently, the proposal of graph convolutional networks (GCN) has successfully implemented into hyperspectral image data representation and analysis. In spite of the great success, there are still several major challenges in hyperspectral image classification, including within-class diversity, and between-class similarity, which generally degenerate hyperspectral image classification performance. To address the problems, we propose a discriminative graph convolution networks (DGCN) for hyperspectral image classification. This method introduces the concepts of within-class scatter and between-class scatter, which respectively reflect the global geometric structure and discriminative information of the input space. The experimental results on the hyperspectral data sets show that the proposed method has good classification performance.

最近，图卷积网络 (GCN) 的提议已成功实施到高光谱图像数据表示和分析中。尽管取得了巨大的成功，但高光谱图像分类仍然存在几个主要挑战，包括类内多样性和类间相似性，这通常会降低高光谱图像分类性能。为了解决这些问题，我们提出了一种用于高光谱图像分类的判别图卷积网络（DGCN）。该方法引入了类内散布和类间散布的概念，分别反映了输入空间的全局几何结构和判别信息。在高光谱数据集上的实验结果表明，该方法具有良好的分类性能。