## 1 Introduction  
Hyperspectral imaging technology combines imaging technology with spectral technology and has achieved wide application in recent years. With the advancement of hyperspectral imaging technology, hyperspectral imaging systems can simultaneously acquire abundant spectral information and two-dimensional spatial information of a feature and then form a hyperspectral image (HSI) [1; 2; 3]. Therefore, hyperspectral imaging technology has become a hotspot for research due to its rich spectral and spatial information. An HSI provides from tens to hundreds of continuous spectral bands [4]. The abundance of spectral information greatly enhances the ability to distinguish objects. Therefore, an HSI is commonly used in disaster monitoring, vegetation classification, fine agriculture, and medical diagnosis due to its extremely high spectral resolution [1; 2; 5]. As the focus of the field of hyperspectral image analysis, the HSI classification task has always received significant attention from scholars. Hyperspectral image classification aims to classify each pixel point in the image [6]. In the early days, most HSI classification methods mainly relied on some traditional machine learning algorithms [7] which were mainly divided into two processes: traditional manual feature engineering and classifier classification [8]. Feature engineering aims to process data based on knowledge so that the processed features can be better used in subsequent classification algorithms. Commonly used feature engineering methods include principal component analysis (PCA), independent component analysis (ICA), and other dimensionality reduction methods. Typical classification algorithms include the support vector machine (SVM) [9], random forest (RF) [10], and k-nearest neighbor (KNN) [11], etc. [12; 13]. The above machine learning approaches only focus on the spectral information of an HSI. It is inaccurate to use the spectral information only for the classification task, thus limiting the improvement in the classification accuracy and the gradual elimination of spectral information. As a result of the triumph of deep learning in areas such as computer vision, many approaches based on deep learning have also been adopted for hyperspectral image classification [14]. Among the deep learning methods, convolutional neural networks (CNNs) [15] have become a popular method for hyperspectral image classification due to their excellent performance. Deep-learning-based methods represented by CNNs have replaced traditional machine-learning-based HSI classification methods and have become a research hotspot [16]. Deep learning methods of 1D-CNN [17] and 2D-CNN were first applied to hyperspectral image classification, and the performance surpassed machine learning methods. However, the above methods suffer from the underutilization of spatial and spectral information. Therefore, the 3D-CNN model [16] was proposed, which can extract spatial-spectral features simultaneously and therefore obtain better classification results, but the model has a large computational burden. To extract richer features, some scholars have proposed a hybrid spectral CNN (HybridSN) [18] which combines 3D-CNN and 2D-CNN to exploit the spatial-spectral features of an HSI with less computational burden than 3D-CNN. With the purpose of finding correlations between data, highlighting important features, and ignoring irrelevant noise information, an attention mechanism has been proposed. [PERSON] et al. proposed a two-branch double attention network (DBDA) [19] which contained two branches to extract spatial and spectral features and added an attention mechanism to obtain better classification results. In order to capture richer features, deeper network layers are needed, but the deeper network layers will lead to computational complexity and make the model training difficult. [PERSON] et al.