为实现油桃品种的快速且无损鉴别,对油桃高光谱图像中的光谱和图像信息进行分析。在光谱信息提取中,采用偏最小二乘回归(partial least squares regression,简称PLSR)从全波段光谱数据提取9个特征波长。在图像信息获取中,采用主成分分析...为实现油桃品种的快速且无损鉴别,对油桃高光谱图像中的光谱和图像信息进行分析。在光谱信息提取中,采用偏最小二乘回归(partial least squares regression,简称PLSR)从全波段光谱数据提取9个特征波长。在图像信息获取中,采用主成分分析(principal component analysis,简称PCA)获得主成分图像,并提取主成分图像的Gabor纹理特征。分别建立基于特征波长光谱特征、主成分图像纹理特征和光谱纹理特征融合的最小二乘支持向量机(least squares support vector machine,简称LS-SVM)和极限学习机(extreme learning machine,简称ELM)油桃品种判别模型。结果表明,基于融合特征的LS-SVM和ELM模型识别率分别为94.7%、92.1%,较单独采用光谱信息和纹理信息的识别率都高,说明采用光谱信息和Gabor纹理信息融合的方法可以实现油桃品种判别,为农产品无损检测提供参考价值。展开更多
The hyper-spectral image contains spectral and spatial information,which increases the ability and precision of objects classification.Despite the classification value of hyper-spectral imaging technology within vario...The hyper-spectral image contains spectral and spatial information,which increases the ability and precision of objects classification.Despite the classification value of hyper-spectral imaging technology within various applications,users often find it difficult to effectively apply in practice because of the effect of light,temperature and wind in outdoor environment.This research presented a new classification model for outdoor farmland objects based on near-infrared(NIR)hyper-spectral images.It involves two steps including region of interest(ROI)acquisition and establishment of classifiers.A distance-based method for quantitative analysis was proposed to optimize the reference pixels in ROI acquisition firstly.Then maximum likelihood(ML)and support vector machine(SVM)were used for farmland objects classification.The performance of the proposed method showed that the total classification accuracy based on the reference pixels was over 97.5%,of which the SVM-M model could reach 99.5%.The research provided an effective method for outdoor farmland image classification.展开更多
基金supported by the Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing under Grant No.2016CP01,Xi’an University of Technology,Xi’an Science and Technology Plan Projects under Grant No.NC1504(2)the National Natural Science Foundation of China under Grant No.31101075+1 种基金the National High Technology Research and Development of China(863 Program)under Grant No.2013AA10230402,Natural Science Fundamental Research Plan of Shaanxi Province under Grant No.2016JM6038Fundamental Research Funds for the Central Universities,NWSUAF,China,Grant No.2452015060.
文摘The hyper-spectral image contains spectral and spatial information,which increases the ability and precision of objects classification.Despite the classification value of hyper-spectral imaging technology within various applications,users often find it difficult to effectively apply in practice because of the effect of light,temperature and wind in outdoor environment.This research presented a new classification model for outdoor farmland objects based on near-infrared(NIR)hyper-spectral images.It involves two steps including region of interest(ROI)acquisition and establishment of classifiers.A distance-based method for quantitative analysis was proposed to optimize the reference pixels in ROI acquisition firstly.Then maximum likelihood(ML)and support vector machine(SVM)were used for farmland objects classification.The performance of the proposed method showed that the total classification accuracy based on the reference pixels was over 97.5%,of which the SVM-M model could reach 99.5%.The research provided an effective method for outdoor farmland image classification.
