河南理工大学学报：自然科学版2011，Vol.30Issue(3)：304-309,6.
基于SVM不同核函数的多源遥感影像分类研究
Research on multi-source remote sensing image classification based on SVM different kernel functions
摘要
Abstract
The paper uses multi-spectral image and hyperspectral image of the same time in the same area as the research target,and employs four different kernel functions of SVM classification algorithm to make experiments between these two images based on the premise that the research has the same test samples and identifying samples.The experiments show that for multi-spectral image,RBF kernel function classification will produce the maximum classification precision,while Sigmoid is at its minimum;for hyperspectral images,Linear kernel function will achieve the maximum classification precision,and Sigmoid is at its minimum;for the same resolution remote sensing images at the same area,on the condition of the same classification standard,the classification precision of multi-spectral image is similar to that of hyperspectral image.关键词
SVM/核函数/多源遥感影像分类Key words
support vector machine/kernel function/multi-source RS image分类
信息技术与安全科学引用本文复制引用
王双亭,艾泽天,都伟冰,康敏..基于SVM不同核函数的多源遥感影像分类研究[J].河南理工大学学报：自然科学版,2011,30(3):304-309,6.基金项目
国家重点基础研究发展计划项目 （）
