For the reasonable and effective collection of Ophiocordyceps sinensis,a new method of on-site identification was attempted using a portable multispectral imaging(MSI)technique.Three dimensional(3D)data-cubes of repre...For the reasonable and effective collection of Ophiocordyceps sinensis,a new method of on-site identification was attempted using a portable multispectral imaging(MSI)technique.Three dimensional(3D)data-cubes of representative Ophiocordyceps sinensis and weeds samples were acquired and pre-processed with standard normal variate transformation(SNV).Principal component analysis(PCA)and simulated annealing particle swarm optimisation(SAPSO)algorithms were used to extract characteristic images and develop the support vector classification(SVC)models.Results show that the fused feature model of SAPSO-SVC has the best performance,resulting in a recognition accuracy of the prediction set of 96.30%.Moreover,on-site distribution map of Ophiocordyceps sinensis and weeds was created using the spectral feature model of SAPSO-SVC,and the target could be easily identified from the distribution map.This work demonstrates the potential for on-site identification of Ophiocordyceps sinensis in the Qinghai-Tibet Plateau using a portable MSI technique combined with the SAPSO-SVC algorithm.展开更多
基金This work was funded by the Fundamental Research Funds for the Central Universities(2017TC016)and the National Key Research and Development Program of China(2016YFE0112800).
文摘For the reasonable and effective collection of Ophiocordyceps sinensis,a new method of on-site identification was attempted using a portable multispectral imaging(MSI)technique.Three dimensional(3D)data-cubes of representative Ophiocordyceps sinensis and weeds samples were acquired and pre-processed with standard normal variate transformation(SNV).Principal component analysis(PCA)and simulated annealing particle swarm optimisation(SAPSO)algorithms were used to extract characteristic images and develop the support vector classification(SVC)models.Results show that the fused feature model of SAPSO-SVC has the best performance,resulting in a recognition accuracy of the prediction set of 96.30%.Moreover,on-site distribution map of Ophiocordyceps sinensis and weeds was created using the spectral feature model of SAPSO-SVC,and the target could be easily identified from the distribution map.This work demonstrates the potential for on-site identification of Ophiocordyceps sinensis in the Qinghai-Tibet Plateau using a portable MSI technique combined with the SAPSO-SVC algorithm.
