## 1 Introduction  
Hyperspectral image classification plays one of the most fundamental and important roles in remote sensing. It uses computers and other tools to quickly classify each pixel in an image into different classes, so as to achieve the ground observation and object recognition. Unlike a two-dimensional color image, Hyperspectral imaging (HSI) is a three-dimensional data cube with hundreds of narrow and continuous spectral bands, providing great potential for the subsequent information extraction [1, 2]. In HSI, each spectral band is an ordinary two-dimensional image, and each pixel almost corresponds to a continuous spectral curve. The spectral curves of each land-cover class vary due to their different reflectance to light of various frequencies, which means that HSI classification assigns a specific set of categories to each pixel based on its spectral information [3]. However, the high dimension of HSI easily leads to the problem of the curse of dimensionality, which increases the complexity of calculation and decreases the accuracy of classification. In addition,HSI data usually contains a small number of labelled samples and the sample distribution is not balanced, easily resulting in an overfitting problem for the class with fewer samples. Due to the inherent characteristics of hyperspectral images, HSI classification is facing great difficulties. Various methods have been proposed to classify HSI, such as the K-nearest neighbor (KNN) algorithm [4], partial least squares-discriminant analysis (PLS-DA) [5], discriminant analysis (DA) or soft independent modeling of class analogy (SIMCA) [6], random forest (RF) [7], support vector machine (SVM) [8; 9], and extreme learning machine (ELM) [10]. However, most of these traditional algorithms encounter the \"curse of dimensionality\". Various methods have been developed to deal with HSI classification problems [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24]. In recent years, many research results in image classification have been obtained with deep learning methods, especially convolutional neural networks. These exciting results demonstrate its powerful feature extraction capabilities in computer vision competition, which brings great opportunities for the development of HSI classification [25]. In 2015, [PERSON] et al. [26] trained a one-dimensional CNN to directly classify a pixel of a hyperspectral image and obtained 92.56% accuracy on the dataset from Pavia University. The architecture of the network was very simple, with only five layers. In 2016, a contextual deep CNN was used to classify HSI by [PERSON] et al. [27], which obtained 94.06% accuracy in the same dataset. In 2017, [PERSON] et al. used one-dimensional and two-dimensional CNNs to classify crops, and they concluded that the effect of two-dimensional CNN was better than one-dimensional CNN [28]. Recently, classification methods based on spectral-spatial methods have made great progress in HSI classification, showing that they have higher classification accuracy, such as the methods proposed in the papers [29; 30; 31; 32]. Although these methods above, based on spectral information classification, can classify HSI effectively, most of them did not consider either dimension reduction of data or spatial information in HSI, likely leading to many noisy points in the classification maps and heavy computation. In a similar way, in this paper, we introduce a novel classification algorithm based on two-dimensional CNN that combines spectral and spatial features. The main contributions of this paper are listed below. 1. To reduce the correlation between HSI spectral bands and the amount of computation, the principle component analysis (PCA) method is used to preprocess the HSI data. 2. Spatial and spectral features are combined ahead of feeding into the classification model. 3. To fully extract the most important information and reduce the risk of overfitting, multi-scale kernels are applied to the first convolutional layer. 4.