高光谱成像
卷积神经网络
模式识别(心理学)
人工智能
特征(语言学)
多光谱图像
遥感
计算机科学
地理
哲学
语言学
作者
Xinna Jiang,Quancheng Liu,Yan Lei,Xingda Cao,Yun Chen,Yuqing Wei,Fan Wang,Hong Wei Xing
标识
DOI:10.1016/j.jfca.2024.106259
摘要
Geographical origin significantly influences the composition of Chinese wolfberries. Establishing a rapid and effective model for origin traceability is crucial. In this study, visible-near infrared (Vis-NIR) hyperspectral imaging technology combined with the proposed Spectral-Imagery Feature Fusion Convolutional Neural Network (S-IFCNN) was developed to identify the origin of wolfberries for the first time. The process began with segmenting adherent wolfberries using a combination of minimum transformation and the watershed algorithm, enabling the extraction of both spectral and image data. We compared various preprocessing methods, input data types, and convolutional kernel sizes to assess their impact on the performance of 1DCNN and 2DCNN models. The optimal configurations were then integrated into the S-IFCNN for a comprehensive evaluation. The study demonstrates the S-IFCNN model's capability in leveraging spectral and image data from hyperspectral imaging for accurately identifying the geographical origins of wolfberries, achieving an accuracy of 91.99%. This research not only highlights the potential applications of the S-IFCNN model but also provides a theoretical framework for the use of hyperspectral imaging in tracing the geographical origins of wolfberries.
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