In hyperspectral image (HSI) classification task, semisupervised graph convolutional network (GCN)-based methods have received increasing attention. However, two problems still need to be addressed. The first is that the initial graph structure in the GCN-based methods is not sufficiently flexible to encode the homogenous structure similarity of HSI pixels when facing the complex scenarios induced by the spatial variability. Another problem is that the input (graph structure) and output (output features) of the GCN-based methods are separated with a “single pass” procedure, which is a suboptimal problem for HSI classification because it does not flexibly optimize the graph construction with a feedback method via output features. In this article, a novel spatial–spectral unified adaptive probability GCN (SSAPGCN) method is proposed for HSI classification. First, considering the homogeneous structural similarity of the pairwise relationships of HSI pixels, this article combines the inherent spectral information and spatial coordinates to obtain the spatial–spectral adaptive probability graph (SSAPG) structure, which can capture the probabilistic connectivity between each pair of the homogeneous HSI pixels. Second, the SSAPG structure and GCN model are combined into a unified framework to a daptively learn both the graph structure and the output features simultaneously with feedback. Finally, the proposed SSAPGCN method with two layers is evaluated on four public HSI datasets to demonstrate its superiority over different classification methods in terms of two evaluation metrics, the overall accuracy (OA) and kappa coefficient (KC), especially with small training sample sizes.

中文翻译：

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用于高光谱图像分类的空间光谱统一自适应概率图卷积网络

在高光谱图像（HSI）分类任务中，基于半监督图卷积网络（GCN）的方法受到越来越多的关注。然而，仍有两个问题需要解决。首先，当面对空间变化引起的复杂场景时，基于 GCN 的方法中的初始图结构不够灵活，无法编码 HSI 像素的同质结构相似性。另一个问题是，基于 GCN 的方法的输入（图结构）和输出（输出特征）通过“单通道”过程分离，这对于 HSI 分类来说是一个次优问题，因为它不能灵活地优化图构造通过输出特征的反馈方法。在本文中，提出了一种新的空间-谱统一自适应概率 GCN (SSAPGCN) 方法用于 HSI 分类。首先，考虑到HSI像素成对关系的同质结构相似性，本文结合固有的光谱信息和空间坐标，获得空间光谱自适应概率图（SSAPG）结构，该结构可以捕获每对像素之间的概率连通性同质 HSI 像素。其次，将 SSAPG 结构和 GCN 模型组合成一个统一的框架，通过反馈同时自适应地学习图结构和输出特征。最后，在四个公共 HSI 数据集上对所提出的两层 SSAPGCN 方法进行了评估，以证明其在两个评估指标方面相对于不同分类方法的优越性：