## 1 Introduction
introduced a residual structure based on the 3D-CNN model [20], constructed a spectral residual module and a spatial residual module, and achieved more satisfactory classification results. Although the classification results achieved by CNN-based classification methods have been good, there are still some limitations. First, the CNN is designed for Euclidean data, and the traditional CNN model can only convolve regular rectangular regions, so it is difficult to obtain complex topological information. Second, CNNs cannot capture and utilize the relationship between different pixels or regions in hyperspectral images; they can only extract detailed features in the local fine region, but the structure features and dependency relationship between the nodes may provide useful information for the classification process [21; 22]. In order to obtain the relationship between objects, graph convolutional networks (GCNs) have been developed rapidly in recent years [23]. GCNs are designed to process graph-structured data. CNNs are used for processing Euclidean data such as images, which are a regular matrix. Therefore, no matter where the convolution kernel of a CNN is located in the image, the consistency of the result of the operation is guaranteed (translational invariance). However, the graph-structured data are non-Euclidean data, and the graph structure is irregular, so it is impossible to apply the CNN on graph data. The graph convolution is designed to resolve this situation. The most important innovation of the GCN is to overcome the inapplicability of translation invariance on non-Euclidean data, so it can be applied to extract the features of the graph structure. [PERSON] et al. proposed the GCN model [24] which is able to operate on non-Euclidean data and extract the structural relationship between different nodes [21]. Some scholars have attempted to apply the GCN to hyperspectral classification tasks [25], and various studies have shown that the classification results are not only affected by spectral information but are also related to the spatial structure information of the pixels [22; 26]. By treating each pixel or superpixel in the HSI as a graph node, the hyperspectral image can be converted into graph-structured data, and then the GCN can be used to obtain the spatial structure information in the image and provide a more effective information for the classification. [PERSON] et al. [22] proposed the MiniGCN method and constructed an end-to-end fusion network which was able to sample images in small batches, classify images as subgraphs, and achieve good classification results. [PERSON] et al. proposed MDGCN [27], which is different from the commonly used GCN. Working on a fixed graph model, MDGCN is able to make the graph structure update dynamically so that the two steps benefit each other. In addition, we cannot consider each pixel of an HSI as a graph node due to the limitation of computational complexity, so hyperspectral images are usually preprocessed as superpixels. The superpixel segmentation technique is applied to the construction of the graph structure, which reduces the complexity of model training significantly. However, the superpixel segmentation technique leads to another problem. Superpixel segmentation often leads to smooth edges of the classification map and a lack of local detailed information of the features. This problem restricts the improvement of the classification performance and has an impact on the analysis of the results. To obtain the relational features of an HSI and to solve the problem of missing details due to superpixel segmentation, inspired by [28], we designed a feature fusion of the CNN and GCN (FCGN). The algorithm consisted of two branches: the GCN branch and CNN branch. We applied the superpixel segmentation technique in the GCN branch. The superpixel segmentation technique can aggregate similar pixels into a superpixel. Then, we treated these superpixels as graph nodes.