高光谱成像
卷积神经网络
计算机科学
模式识别(心理学)
人工智能
遥感
地理
作者
Xinhui Zhao,Haotian Que,Xiu‐Lan Sun,Qibing Zhu,Min Huang
标识
DOI:10.1016/j.infrared.2022.104270
摘要
• Hyperspectral imaging combined with CNN model is used for wheat seed classification. • A hybrid convolutional approach is proposed to extract spectral and spatial features. • The hybrid features are suitable for hyperspectral images and achieve the best result. Seed quality and safety are related to national food security, and seed variety purity is an important indicator in seed quality detection. In agricultural production, seeds of higher purity are more accessible with stable genetic traits. Thus, it is necessary to rapidly and non-destructively detect the seed purity. In this study, a novel hybrid convolutional network coupled with hyperspectral imaging was used for the sorting of wheat seeds. Hyperspectral imaging acquires spectral and spatial information simultaneously, which provides a collection of internal and external features of samples. To take full advantage of the spatial and spectral information, the hyperspectral images of wheat seeds were analyzed by hybrid convolution. Firstly, the average spectra were extracted from the region of interests (ROIs) and processed with one-dimensional (1D) convolution to extract spectral features. Meanwhile, the two-dimensional (2D) convolution was used to directly analyze hyperspectral images focusing more on extracting spatial features. Then, the features extracted by hybrid convolutional were integrated into the full connection layer of the network structure. The performance of the proposed models was validated with 8000 samples from the eight varieties of wheat seed. Experiments demonstrate that the hybrid convolutional network outperforms the reference model and gets excellent classification accuracy of 95.65% for wheat seeds.
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