## 1 Introduction  
With the development of remote sensing technology in the late 20 th century, the emergence of hyperspectral imagery (HSI) technology has attracted widespread attention in various fields. Unlike natural images and multispectral images, the resolution of hyperspectral images in the spectral range is very high, generally in the order of \(10^{-2}\lambda\). A hyperspectral imager can record hundreds of channels of spectral band information from ultraviolet to midinfrared. This rich spectral band information has become the focus of many fields such as land monitoring, crop growth, petroleum exploration, and military target recognition. Hyperspectral imagery classification (HSIC) is more difficult than natural image classification for two primary reasons [1]:  
1. The spectral bands of hyperspectral images have high dimensionality, which increases the computational complexity. Moreover, according to the Hughes phenomenon, as the dimension number increases, the classification accuracy first increases and then decreases. 2. The spatial resolution of hyperspectral images is very low, and one pixel typically represents a distance of tens of meters. Therefore, the number of pictures in each dataset is very small, and extracting spatial features is more difficult. At present, there are several different ideas to solve the problem of excessive dimensionality of hyperspectral datasets. First, the dimensionality of the spectral band can be reduced using one of the two broad strategies: projection and manifold learning. Principal component analysis [2] is a typical linear dimensionality reduction method in projection. The purpose is to map high-dimensional data to a low-dimensional space through a certain linear projection. In some high-dimensional images with more noise, the effect of principal component analysis is not satisfactory. Specifically, when the variance of the noise contained in a principal component with a large amount of information is greater than the variance of the signal, the quality of the image formed by the principal component is poor. [PERSON] [3] et al. addressed this problem using the MNF (minimum noise fraction) rotation. It essentially includes two stacked principal component transformations. When faced with some manifold data such as Swiss rolls, projection-based dimensionality reduction methods cannot solve the problem well; therefore, manifold learning is a better choice. The main methods are Isomap algorithm [4], Laplacian eigenmaps [5], and so on. In addition to dimensionality reduction methods, 3D-CNN in deep learning has also been applied to hyperspectral datasets. [PERSON] [6] and [PERSON] [7] used 3D-CNN to directly extract advanced features in hyperspectral images and realized the classification of hyperspectral images, cleverly avoiding the high dimensionality of hyperspectral images. However, the computational complexity of 3D-CNN is relatively large, and the training time of neural networks using 3D-CNN is relatively long. To fully extract the effective feature information in the hyperspectral image, people try to combine different methods to think about the features in the spectral domain and spatial domain. In traditional machine-learning methods, some typical classifiers, such as k-nearest neighbors [8], support vector machines [9; 10], etc., used the characteristic information of the spectral domain as the division standard and adopted different methods to effectively classify hyperspectral images. The powerful feature extraction ability makes deep learning attractive for traditional optical problems [11; 12; 13] and hyperspectral classification problems. Stacked AutoEncoders (SAE) [14] used the fusion feature information of the spectral domain and the spatial domain as training features for the classification of hyperspectral images. This is the first time that deep learning has been applied to the classification of hyperspectral images.