## 1 Introduction
From the long-range and multi-angle levels, this model can acquire pixel-level and super-pixel-level features simultaneously [34]. The CNN refines classification tasks by conducting local convolution, and the GCN acquires long-range similarity relationships through global connections. However, the application of GCNs on HSIs is not a good enough fit. Traditional CNNs and GCNs collectively lack consideration for the structural relationships present within network data. When fusing different models and features, most methods usually use additive (A), elementwise multiplicative (M), and concatenation (C) processes. Regarding network connections, the majority of them are typically interpreted as local connections. Analysis shows that CNNs and GCNs have their respective strengths from different perspectives. At the same time, their fusion strategies need to be further studied. The following concerns are worthy of our consideration. (1) HSIs encompasses rich ground features. However, existing methods often overlook the correlation between spatial and spectral aspects. The challenge lies in achieving a balanced utilization of spatial and spectral information, making the method of combining local and global HSI information an area worthy of further exploration. (2) Most GCN-based methods focus on extracting spatial relationships between image data. These frameworks use adjacent pixels to represent adjacency relationships. However, the advantage of HSIs lies in the spectral dimension, where the expression of spatial relationships is limited and requires a more relevant, new expression of relationships. (3) GCNs require the adjacency matrix of the original data as input, so GCNs only allow for full-batch network learning. When the hyperspectral data are large, the one-time data input is demanding on the hardware equipment. There may be negative effects, such as slow gradient descent. To address the above problems, we propose a staged feature fusion network architecture that combines CNNs and GCNs, called SFFNet. Simply put, we first perform random sampling on HSIs while obtaining the central node for classification and the image blocks for auxiliary classification. These are fed into the CNN convolution module to extract features and enhance local information. After extracting the corresponding features, we construct a graph data structure and send it to the GCN module, updating the relationship between node features based on spectral similarity. Classification is completed after outputting the final predicted probability. SFFNet can make full use of the spatial-spectralinformation of HSIs to strengthen the global information. The main contributions of this paper are as follows. (1) A HSI classification model connecting a CNN and GCN is proposed, which is called SFFNet. The CNN refines the original information and the GCN obtains the information correlation. By improving the spectral-spatial feature-awareness capability of the model, the model can achieve high-precision HSI classification. (2) Optimizing the connectivity relationships of GCNs based on the similarity of spectral dimensions. The introduction of the GCN addresses the limitation of traditional CNNs, which can only extract local features using fixed convolution kernels. This approach combines both the local and global information of images. (3) Allowing the GCN to input data in batches. Following feature extraction by the CNN, the GCN constructs information node similarities. Through multiple iterations, the network weights can be optimized. This approach reduces hardware requirements while ensuring classification accuracy. The remainder of this paper is organized as follows. Section 2 begins with an introduction to related work. Section 3 describes our image classification framework based on combining the CNN and GCN in detail. Section 4 organizes the background and settings related to the experiments. We will give an analysis of the experimental results in Section 5. Finally, the whole manuscript will be summarized in Section 6.