## 1 Introduction  
The advancement of remote sensing technology is incrementally moving from conventional multispectral imaging, which operates within the visible and infrared spectra, to hyperspectral imaging, which encompasses hundreds of contiguous spectral bands. The essence of this shift is rooted in hyperspectral imaging's capacity to record the spectral reflectance of targets across a wide array of bands, yielding a more exhaustive and intricate set of spectral features for ground objects compared to multispectral imagery [1]. The high-dimensional characteristic of hyperspectral data empowers more precise identification of ground object categories during classification tasks and allows for a nuanced differentiation of the objects' condition and composition. This enhances the practical value of remote sensing data substantially. Hyperspectral imaging technology is applied broadly across various fields, including environmental monitoring, agricultural management, mineral exploration, urban planning, and military reconnaissance. It provides detailed recognition of ground objects, monitoring of their states, compositional analysis, and sophisticated management support. In the face of challenges such as substantial data volumes, intricate processing requirements, and higher costs, the wealth of information and the broad range of potential applications render hyperspectral imaging technology a pivotal area of development in the realm of remote sensing [2]. However, the high-dimensional nature of hyperspectral images presents significant challenges in data processing [3]. Traditional image classification methods often encounter the \"curse of dimensionality\" when dealing with these high-dimensional data, where the required number of training samples increases exponentially with the dimensionality of the data, leading to a sharp rise in computational complexity and a decline in classification performance. To overcome these challenges, researchers have continuously explored and developed new algorithms to enhance the accuracy of hyperspectral image classification. These algorithms span various domains, including statistical learning-based methods that employ feature selection and dimensionality reduction techniques to reduce data dimensions while preserving critical information; machine learning-based methods such as Support Vector Machines (SVM) and Random Forests (RF), which can handle nonlinear relationships and improve classification performance; and recently emerging deep learning methods such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), which can automatically extract complex spectral features through multi-level feature learning, demonstrating superior performance in processing hyperspectral data. The continuous progress and innovation of these algorithms bring new hope to the field of hyperspectral image classification, enabling us to more effectively utilize these valuable remote sensing data. The classification of hyperspectral imagery indeed necessitates innovative neural network architectures to confront difficulties such as intricate spectral characteristics, the amalgamation of both global and local attributes, the need for computational effectiveness, and the ability to encapsulate multi-scale details. Hyperspectral images (HSI) encompass a multitude of spectral channels, with each pixel possessing an abundance of spectral data. Conventional neural network architectures, like CNNs, frequently face challenges in discerning the nuanced discrepancies and complex inter relationships within the spectra of high-dimensional data, thereby constraining classification precision. HSI necessitates not only the identification of local spatial attributes but also the integration of overall spectral traits. However, standard CNN models predominantly concentrate on local spatial features and are deficient in effectively modeling global spectral attributes.