Fast 3D-CNN Combined with Depth Separable Convolution for Hyperspectral Image Classification

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%.

Keywords

• |hyperspectral image (hsi) classification|spatial spectrum feature extraction|3-dimensional convolutional neural networks (3d-cnn)|depthwise separable convolution (dsc)|deep learning