## 1 Introduction  
In the realm of remote sensing imagery, hyperspectral images distinguish themselves from traditional images by encompassing hundreds of contiguous spectral bands [1]. Each pixel within an hyperspectral image carries a wealth of spectral information that reflects the unique spectral characteristics of various objects [2]. The rich spectral and spatial information embedded in hyperspectral images greatly augment the potential for fine-grained object classification. Through its superior object representation and precise feature recognition capabilities, hyperspectral image data allow for the assignment of a distinct label to each pixel, facilitating accurate land cover classification. This classification technique finds broad applications in precision agriculture [3, 4], environmental monitoring [5], mineral exploration [6], urban planning [7], land scene classification [8], and other fields. Traditional machine learning methods such as K-Nearest Neighbors (KNN) [9], Support Vector Machine (SVM) [10], Random Forests (RF) [11] have been widely employed in hyperspectral image classification. For instance, [PERSON] et al. [12] employed bilateral filtering to extract spatial features and utilized SVM to integrate spectral and spatial features for hyperspectral image classification, but encountered challenges in extracting essential features from high-dimensional hyperspectral image data. These shallow traditional machine learning methods exhibit certain limitations when faced with the high dimensionality and diverse nature of spectral features. Since the emergence of deep learning in 2006, extensive research and application have been conducted in computer vision and remote sensing science [13]. The widespread use of deep learning methodologies in hyperspectral image classification has shown improved performance compared to traditional machine learning approaches [14]. Various studies have demonstrated the exceptional classification performance of the convolutional neural network (CNN) in hyperspectral image classification [15; 16; 17; 18]. [PERSON] et al. [19] proposed leveraging spectral features for hyperspectral image classification by employing CNNs to classify pairs of pixels comprising central and surrounding pixels. Hyperspectral images not only contain abundant spectral information but also encompass crucial spatial features. In order to enhance the integration and utilization of spectral features, [PERSON] et al. [20] initially extracted spectral features before spatial features and deepened the network through residual connections to extract more profound contextual information. Moreover, [PERSON] et al. [21] developed a dual-branch attention mechanism network to separately extract spectral and spatial features, thereby preventing mutual influence between the two during extraction. Additionally, [PERSON] et al. [22] applied a multi-scale spectral-spatial joint network to extract features at various granularities, enhancing the performance of hyperspectral image classification. [PERSON] et al. [23] achieved promising results in small-sample hyperspectral image classification by utilizing a dual-branch structure to extract multiscale spatial and spectral information. In the field of hyperspectral image classification, the advancements in deep learning frameworks centred around CNNs have highlighted their effectiveness in extracting local features. Nevertheless, the limited receptive field of CNNs, constrained by kernel size and layer count, restricts the model to only model and analyze a finite receptive field [24]. This limitation impedes the model from capturing global context information and fails to model long-range dependencies adequately, particularly in the context of hyperspectral images with numerous spectral bands [25]. The deficiency in global representation capacity leads CNN models to overlook global spatial and spectral features that could contain crucial discriminative information.