###### Abstract
The high spatial resolution of hyperspectral images allows for a very small number of hybrid pixels and provides clear boundaries between different objects [13]. Spatial features, such as morphological features, can provide high classification accuracy. Recent studies show that the classification accuracy of hyperspectral images using deep learning is high in practice [14]. Deep learning was developed on the basis of machine learning (through the study of artificial neural networks); a machine mimics the information processing structure of the human brain [15]. Deep learning often involves multiple levels of neural network structures, and a neural network automatically learns from a large number of samples during stepwise training to obtain deep features from the data [16]. As typical models in the field of deep learning, convolutional neural networks (CNNs) are vital in the field of computer vision. A CNN is a deep feedforward neural network with a convolutional structure in which neurons can respond to cells in a region with a central point; CNNs process large images well and are widely used in the field of image processing [17]. [PERSON] et al. [18] used probabilistic principal component analysis (PCA) and Gabor filters to extract spectral and spatial features, respectively, and fused these features into their designed 2D CNN framework for classification. [PERSON] et al. [19] proposed a fast 3D CNN model to extract spatial-spectral features and improve hyperspectral image classification performance. [PERSON] et al. [20] proposed a deep CNN with a two-branch structure; they used one-dimensional (1D) convolution and 2D convolution to extract spectral and spatial features, respectively, and combined them for classification. Driven by ResNet, [PERSON] et al. [21] designed a spatial-spectral residual network (SSRN) to classify hyperspectral images and obtained good results. [PERSON] et al. [22] proposed an end-to-end fast dense spectral-spatial convolution network framework. Different convolution kernels were used to extract multiscale spectral-spatial features, and features at multiple scales were extracted. Both 1D and 2D convolution can be used to extract spectral and spatial features, but the spectral-spatial relationship is ignored. Three-dimensional convolution can directly extract spectral-spatial features, but the use of 3D convolution itself complicates calculations. Therefore, [PERSON] et al. [23] proposed a model that stacks 3D and 2D convolutional layers to make full use of spectral and spatial features to improve classification accuracy. CNN-based methods have achieved good results in the field of hyperspectral image classification, but the contribution of the feature map outputs of each convolutional layer of classification is different. The performance and generalization ability of a model should be improved to enable the neural network to focus selectively on important information in the input. [PERSON] et al. [24] constructed a so-called squeeze incentive network and achieved remarkable results in the 2017 Large-Scale Visual Identity Challenge classification competition. [PERSON] et al. [25] proposed a new machine learning-based tool called VULMA (Vulnerability Analysis using Machine Learning) that is able to capture the key features of buildings in existing inventory starting with a simple photo of the building. Their approach gave us a lot of ideas. We found that it is difficult for the traditional hyperspectral remote sensing image classification method to distinguish some highly similar objects effectively in the face of complex objects. Inspired by the above research, we designed an HRSI classification method based on morphological processing (MP) and a 3D-2D CNN to improve the classification accuracy of hyperspectral remote sensing images and the feature discrimination ability of the network:  
1. A new HRSI classification framework was designed. This framework consists of PCA, MP, CNNs, residual connections, and an attention mechanism. 2. A new 3D-2D CNN model was designed.