摘要
高光谱遥感是将目标探测技术与光谱成像技术相结合的多维地物信息获取技术,可以同时获取描述地物分布的二维空间信息与描述地物光谱特征属性的一维光谱信息。相对于多光谱遥感,高光谱图像具有更加丰富的地物光谱信息,可以详细地反映待测地物细微的光谱属性,使地物的精确分类成为可能。本文通过对SVM与RVM的理论研究与对比分析,将这两种高维数据处理算法应用于同一高光谱图像中进行分类研究。实验结果表明,SVM的总体分类精度要略高于RVM的总体分类精度。
Hyperspectral remote sensing is the multi-dimensional information obtaining technology,which combines target detection and spectral imaging technology together.That is,it could obtain the two-dimensional object distribution information and one-dimensional spectral feature characteristic information at the same time.Compare with multi-spectral remote sensing,hyperspectral images contain abundant spectral information for the targets,which could greatly reflect the detailed characteristic of the ground,and makes the precise classification possible.Through the comparison of the theory between support vector machine(SVM)and relevance vector machine(RVM),this study utilized the two high-dimensional data processing methods in the classification of the same hyperspectral imagery.The experiment results show that the overall classification of SVM is slightly higher than that of RVM.
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2013年第S1期143-147,共5页
Journal of Jilin University:Engineering and Technology Edition
基金
国家自然科学基金项目(61077079)
教育部博士学科点专项科研基金项目(20102304110013)
黑龙江省自然科学基金重点项目(ZD201216)
关键词
高光谱图像分类
支持向量机
相关向量机
hyperspectral imagery classification
support vector machine
relevance vector machine
