## 1 Introduction
developed a simple three-dimensional convolutional neural network (3D-CNN) framework for HSI classification, which directly captures spatial and spectral information for hyperspectral processing without requiring any preprocessing [18]. In 2022, [PERSON] et al. added a multi-scale fused spectral attention module to 3D-CNN to improve spectral continuity [19]. In their latest research, [PERSON] et al. proposed a local-spectral feature optimization method using local and global spectral feature extraction blocks to automatically extract raw information [20]. The emergence of CNNs has effectively advanced the field of HSI classification. By extensively exploring the underlying information embedded in images, it becomes possible to discern the correspondence of categories. However, CNNs have the disadvantage of extracting information only in the region of fixed convolutional kernel size. In this case, a CNN cannot capture the global information of a HSI. The information between spatial and spectral aspects is also worthy of further study. Recognizing the limitations of CNNs in HSI processing, researchers have endeavored to introduce a neural network for handling non-Euclidean data, known as the Graph Neural Network (GNN) [21]. Notably, the Graph Convolutional Neural Network (GCN) [22] has assumed an increasingly significant role in this domain [23; 24; 25]\". The GCN is able to constantly update the correlation relationship of nodes by aggregating the information of neighboring nodes. By constructing data relationships across the map, the GCN serves as a bridge for global data exchange. In 2018, [PERSON] et al. proposed a spatial graph of convolutional networks for semi-supervised HSI classification (S\({}^{2}\)GCNs) [23], which utilizes adjacent nodes in the graph to approximate convolution. [PERSON] et al. proposed the MDGCN in 2019 [26]. The model chooses the GCN in order to improve the drawbacks of traditional CNN models that can only perform static computations. In 2020, [PERSON] et al. proposed the CAD-GCN to improve the problem of contextual insensitivity [27]. Based on the traditional GCN, graphs' variable weights and connection relationsare improved. In 2021, [PERSON] et al. proposed a novel semi-supervised network based on graph sample and aggregate-attention (SAGE-A), which utilizes a self-attention mechanism to determine the information of HSIs [28]. The GCN's ability in spatial construction is well documented. It can effectively model the similarity relationship between data. By combining topological information, the classification of HSIs is optimized. However, most of the models are also accompanied by computational complexity and training difficulty. Researchers have gradually fallen into the bottleneck of single network frameworks. It has been realized that fusion between frameworks can be effective for this purpose. [PERSON] et al. proposed a new minibatch GCN in 2020, which allows for data to be trained in a minibatch manner [29]. Through this method, the GCN makes it possible to process large-scale HSI data. [PERSON] et al. proposed a multilevel super-pixel structured graph U-Net (MSSGU) to learn multiscale features on multilevel graphs [30]. They further improved the original framework by incorporating a graph attention mechanism combining 3D-CNN and 2D-CNN. In 2022, [PERSON] et al. proposed a Weighted Feature Fusion of Convolutional Neural Network and Graph Attention Network (WFCG) for HSI classification by using the characteristics of the super-pixel-based GAT and pixel-based CNN [31]. In 2023 study, [PERSON] et al. proposed an adaptive model called the AMGCFN [32]. It includes two sub-networks of multi-scale full CNNs and a multi-hop GCN to extract the multilevel information of HSIs. [PERSON] et al. have proposed a two-branch deeper GCN (TBDGCN) [33] by combining GCN and CNN to extract the pixel-level information of HSIs simultaneously. In theie latest research, [PERSON] et al. make full use of the advantages of CNNs and GNNs and propose a graph-polarized fusion (GPF) model.