Hyperspectral image classification of wolfberry with different geographical origins based on three-dimensional convolutional neural network
| Autor: | Lei Chen, Shenyi Wang, Yanju Guo, Yuchen Zhao, Mu Qingshuang, Zhilong Kang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
hyperspectral image
depth separable convolution Nutrition. Foods and food supply Computer science business.industry Hyperspectral imaging Pattern recognition TP368-456 Convolutional neural network Food processing and manufacture Image (mathematics) Support vector machine three-dimensional convolutional neural network Hyperspectral image classification classification of wolfberry origin support vector machine TX341-641 Artificial intelligence Hypercube business Food Science |
| Zdroj: | International Journal of Food Properties, Vol 24, Iss 1, Pp 1705-1721 (2021) |
| ISSN: | 1532-2386 1094-2912 |
| DOI: | 10.1080/10942912.2021.1987457 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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