Hyperspectral image classification of wolfberry with different geographical origins based on three-dimensional convolutional neural network

Abstract

The hyperspectral image is a three-dimensional (3D) hypercube with spectral and spatial continuity. Traditional hyperspectral imaging (HSI) processing mainly focuses on spectral information. However, this paper proposed a new hybrid convolutional neural network (New-Hybrid-CNN) algorithm using HSI spectral-spatial joint information. We used the algorithm combined with HSI processing to classify the origin of Chinese wolfberry from Ningxia, Qinghai, Gansu, and Xinjiang. (1) Selecting the region of interest (ROI) over the raw HSI data as input; (2) Extracting spectral-spatial joint information from the hyperspectral stack information using homogeneous 3D convolution architecture with $$3 \times 3 \times 3$$ convolution kernels; (3) Then the depth separable convolution (DSC) was used to learn spatial information. This algorithm combined the advantages of 3D convolution and DSC, and it effectively extracted deep spectral-spatial joint information and made the architecture more lightweight. 3D convolutional neural network (3D-CNN), hybrid spectral convolutional neural network (HybridSN), and support vector machine (SVM) were established to compare with the proposed method. The proposed algorithm made full use of the HSI information while reducing the number of parameters and training time involved in the network, and improved the classification accuracy. The classification accuracy of wolfberry origin reached more than 99%. Therefore, the New-Hybrid-CNN classifier combined with HSI had the potential to classify wolfberry origin and food detection.

Keywords

• hyperspectral image
• three-dimensional convolutional neural network
• depth separable convolution
• support vector machine
• classification of wolfberry origin