检索结果-南通市图书馆

Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing

Computer Systems Science & Engineering 2023年第7期46卷 303-319页

作者： Imran Ali Zohaib Mushtaq Saad Arif Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Department of Mechanical Engineering National Taiwan University of Science and TechnologyTaipei City106335TaiwanChina Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Mechanical Engineering HITEC University TaxilaTaxila Cantt.47080Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia

Hyperspectral imaging is gaining a significant role in agricultural remote sensing *** data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information of each pixel in the third *** classification accuracy of hyperspectral images(HSI)increases significantly by employing both spatial and spectral *** this work,the data was acquired using an airborne hyperspectral imager system which collected HSI in the visible and near-infrared(VNIR)range of 400 to 1000 nm wavelength within 180 spectral *** dataset is collected for nine different crops on agricultural land with a spectral resolution of 3.3 nm wavelength for each *** data was cleaned from geometric distortions and stored with the class labels and annotations of global localization using the inertial navigation *** this study,a unique pixel-based approach was designed to improve the crops'classification accuracy by using the edge-preserving features(EPF)and principal component analysis(PCA)in *** preliminary processing generated the high-dimensional EPF stack by applying the edge-preserving filters on acquired *** the second step,this high dimensional stack was treated with the PCA for dimensionality reduction without losing significant spectral *** resultant feature space(PCA-EPF)demonstrated enhanced class separability for improved crop classification with reduced dimensionality and computational *** support vector machines classifier was employed for multiclass classification of target crops using *** classification performance evaluation was measured in terms of individual class accuracy,overall accuracy,average accuracy,and Cohen kappa *** proposed scheme achieved greater than 90%results for all the performance evaluation *** PCA-EPF proved to be an effective attribute for crop classification using hyperspectral imaging in the VNIR *** proposed scheme

关键词： Hyperspectral imaging visible and near-infrared edge preserving feature dimensionality reduction crop classification

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：