## 1 Introduction  
HSIs have been widely used in many fields due to their advantage of recognizing subtle spectral differences [1]. In the field of classification, most of the problems are focused on the \"dimensionality catastrophe\", under-utilization of information, and lack of labeled samples. Researchers have tried to solve these issues by using various methods such as the simplified maximum likelihood classification technique [2], Support Vector Machines (SVMs) [3], and Markov random field (MRF) [4]. The majority of conventional techniques approach problems from a linear standpoint, making it difficult to convey the underlying nonlinear relationship between the original spectrum data and associated materials. With the development of machine learning, deep learning has enabled high-precision classification of HSIs. Deep learning is extremely widely used on HSIs. Among them, the most common network models include the Recurrent Neural Network (RNN), Generative Adversarial Network (GAN), CNN, Transformer, etc. In 2017, [PERSON] et al. used an RNN for the first time to in field of HSI classification. Hyperspectral pixels were analyzed in the pattern of sequential data [1]. In 2019, [PERSON] et al. proposed a cascaded RNN to explore the redundant and complementary information of HSIs [5]. In 2018, [PERSON] et al. first discovered the possibility of using a GAN for HSI classification [6]. With the explosion of Transformer, Visual Transformer (ViT) [7] set the trend in the image processing field. [PERSON] et al. [8] used a bidirectional encoder Transformer to implement HSI classification, with good training results. [PERSON] proposed SpectralFormer, which is a new approach to classifying HSIs from a Transformer's sequence perspective [9]. Different models have different shortcomings in HSI classification. For example, RNNs are prone to performance bottlenecks in large-scale training tasks, and GANs are not easy to debug. Transformer has a high demand for data. As 3D images, HSIs are generally characterized by a large amount of information and large differences in categories. The existing publicly available hyperspectral datasets are also limited. For practical applications based on hyperspectral images, CNN-based methods have emerged as the most widely adopted approach for HSI classification [10]. CNNs include 1D-CNN, 2D-CNN, and 3D-CNN models. These modes differ in computational dimensions and they all utilize backpropagation algorithms for network updates [1]. In 2015, [PERSON] et al. first applied 1D-CNN to HSI classification based on spectral features [11]. [PERSON] et al. introduced Dropout to address overfitting [12]. However, the occurrence of \"same-spectrum different objects\" and \"different objects with the same spectrum\" in HSIs implies that spectral information alone cannot deliver exceptional classification results. To explore the spatial information of HSIs, researchers have experimented with using 2D-CNN. In 2018, [PERSON] et al. employed a CNN for feature extraction and subsequently utilized AdaBoostSVM for band selection [13]. [PERSON] et al. have utilized 2D-CNN for feature extraction and a perceptual machine model for classification [14]. [PERSON] et al. expanded into the spatial domain [15], while [PERSON] et al. achieved deep spatial feature extraction and fusion [16]. 2D-CNN has yielded outstanding outcomes in HSI classification. However, its limitation is that the original image must be downscaled before convolution, which may result in the loss of key information. In order to simultaneously utilize the spatial and spectral information of hyperspectral images, an increasing number of researchers are opting for a \"spatial-spectral\" processing approach. In 2016, [PERSON] et al. proposed the Two-CNN model. This model separates the spatial and spectral channels to extract the information before concatenating the features to complete the classification [17]. In 2017, [PERSON] et al.