## 1 Introduction  
Remote sensing techniques are becoming increasingly mature. Among them, hyperspectral remote sensing is an advanced technology combining imaging technology and spectral technology, based on numerous narrow and contiguous spectral bands [1, 2]. In a hyperspectral image (HSI), each pixel contains spectral information across different bands and spatial information. Regarding the exploitation of distinguishing subtle spectral differences, HSI has a lot of application prospects in cross-scene coastal wetlands [3], precision agriculture [4], and soil carbon content prediction [5]. In recent years, the research on HSI classification has played a significant role in fully utilizing the value of remote sensing images and has drawn broad attention in the field of remote sensing [6]. Compared to traditional methods depending on the handcrafted feature [7, 8, 9, 10, 11], deep learning (DL) has occupied a dominant position in computer vision [12],due to its robust feature representation capabilities and prominent performance. The methods based on DL include recursive autoencoder (RAE) [13], convolutional neural network (CNN) [14; 15; 16; 17], recurrent neural network (RNN) [18; 19], and generative adversarial network (GAN) [20], which have achieved exciting performance. Due to the reliance on spectral features in previous methods, the spatial features were often neglected, resulting in the performance of those models remaining unsatisfactory. Then, more and more classification frameworks based on spectral-spatial features have been developed, which incorporate spatial characteristics of ground objects into pixel-wise classifiers. For instance, a model called HybridSN for HSI classification is explored in ref. [21], which constructs a 2D-CNN and a 3D-CNN to extract more abstract-level feature representation after principle component analysis (PCA) [22] operation on original images, improving the classification effects. The proposed pyramid-based hierarchical spatial-spectral transformer (PyFormer) [23] enhances processing efficiency by organizing input data into hierarchical pyramid segments at different abstraction levels. Each level uses a dedicated transformer encoder to effectively capture local and global contexts. The final input representation integrates outputs from various levels. The pyramid excels at capturing spatial features and local patterns, while the transformer models spatial-spectral correlations and long-range dependencies. One popular structure is decoupling the classification task for two branches to extract the spectral features and spatial features, respectively. [PERSON] et al. [24] present an HSI classification method using a double-branch multi-attention mechanism network (DBMA). The network designs two branches that separately extract spectral features and spatial features, reducing interference between them. Each branch employs a specific attention mechanism suited to its characteristics, ensuring the extraction of more discriminative features. The features from the double branch are then finally fused for classification. Another widely used approach is cropping HSI data into local cubes in a certain size as inputting data and then transferring them to the CNN module for acquiring the joint features [25]. For example, in [26], spatial-spectral information is acquired by feeding data local cubes into a network that combines a 3D CNN with a feature pyramid structure. Additionally, this network employs multiscale fusion to enhance sensitivity to spatial-spectral information. In recent years, many well-recognized networks based on transformers [27] have been widely and successfully applied to HSI classification since Vision Transformer (ViT) [28] was proposed in 2020. For example, [PERSON] et al. [29] proposed a novel backbone network SpectralFormer which is able to learn spectrally local sequence information from neighboring bands of HSI, thereby generating group-wise spectral embeddings.