Abstract:

In the process of feature extraction and classification of hyperspectral images using convolution neural networks, there are problems such as insufficient extraction of spatial spectrum features and too many layers of networks, which lead to large parameters and complex calculations. A lightweight convolution model based on fast three-dimensional convolution neural networks (3D-CNN) and depth separable convolutions (DSC) is proposed.Firstly, incremental principal component analysis (IPCA) is used to preprocess the dimension reduction of the input data. Secondly, the pixels of the input model are divided into small overlapped 3D small convolution blocks, and the ground label is formed on the segmented small blocks based on the center pixel. The 3D kernel function is used for convolution processing to form a continuous 3D feature map, retaining the spatial spectral features. 3D-CNN is used to extract spatial spectrum features at the same time, and then depth separable convolution is added to 3D convolution to extract spatial features again, which enriches spatial spectrum features while reducing the number of parameters, thus reducing the calculation time and improving the classification accuracy. The proposed model is verified on Indian Pines, Salinas Scene and University of Pavia public datasets, and compared with other classical classification methods. Experimental results show that this method can not only greatly save the learnable para-meters and reduce the complexity of the model, but also show good classification performance, in which the overall accuracy (OA), average accuracy (AA) and Kappa coefficient can all reach more than 99%.

Key words: hyperspectral image (HSI) classification, spatial spectrum feature extraction, 3-dimensional convolutional neural networks (3D-CNN), depthwise separable convolution (DSC), deep learning

摘要：

针对卷积神经网络在高光谱图像特征提取和分类的过程中，存在空谱特征提取不充分以及网络层数太多引起的参数量大、计算复杂的问题，提出快速三维卷积神经网络（3D-CNN）结合深度可分离卷积（DSC）的轻量型卷积模型。该方法首先利用增量主成分分析（IPCA）对输入的数据进行降维预处理；其次将输入模型的像素分割成小的重叠的三维小卷积块，在分割的小块上基于中心像素形成地面标签，利用三维核函数进行卷积处理，形成连续的三维特征图，保留空谱特征。用3D-CNN同时提取空谱特征，然后在三维卷积中加入深度可分离卷积对空间特征再次提取，丰富空谱特征的同时减少参数量，从而减少计算时间，分类精度也有所提高。所提模型在Indian Pines、Salinas Scene和University of Pavia公开数据集上验证，并且同其他经典的分类方法进行比较。实验结果表明，该方法不仅能大幅度节省可学习的参数，降低模型复杂度，而且表现出较好的分类性能，其中总体精度（OA）、平均分类精度（AA）和Kappa系数均可达99%以上。

关键词: 高光谱图像分类, 空谱特征提取, 三维卷积神经网络（3D-CNN）, 深度可分离卷积（DSC）, 深度学习