自动化与信息工程2023，Vol.44Issue(6)：33-38,45,7.DOI:10.3969/j.issn.1674-2605.2023.06.006
基于高光谱成像的当归与独活分类
Classification of Angelicae and Heracleum Based on Hyperspectral Imaging
摘要
Abstract
To avoid confusion between Angelicae and Heracleum,deep learning and near-infrared hyperspectral imaging techniques were combined to classify them.Firstly,obtain the average spectral data of Angelicae and Heracleum samples,and use saliency maps to select 20 bands from the average spectral data as feature bands to achieve feature extraction and dimensionality reduction;Then,a one-dimensional convolutional neural networks(1D-CNN)model and a support vector machine(SVM)model were used to classify Angelicae and Heracleum on spectral datasets with a total of 181 bands and 20 bands,respectively.The result of classification showed that when modeling using full band spectral datasets,the accuracy of 1D-CNN and SVM on the test set was 98.6%and 98.1%in classification,respectively;When modeling using the characteristic bands spectral datasets,the accuracy of 1D-CNN and SVM on the test set was 96.1%and 95.5%in classification,respectively.Therefore,combining hyperspectral imaging technology with deep learning can achieve rapid classification of Angelicae and Heracleum.关键词
高光谱成像/一维卷积神经网络/支持向量机/特征波段/分类Key words
hyperspectral imaging/one-dimensional convolution neural networks/support vector machine/characteristic bands/classification分类
信息技术与安全科学引用本文复制引用
赵路路,殷泽轩,陈红,刘诚..基于高光谱成像的当归与独活分类[J].自动化与信息工程,2023,44(6):33-38,45,7.基金项目
国家自然科学基金面上项目(62275056) （62275056）
梅州市应用型科技专项资金项目(2021B0203001). （2021B0203001）
