摘要
以大兴安岭为试验区，提出将主成分分析（PCA）得到的第1分量、归一化植被指数（NDVI）和Landsat TM1～TM7某一波段进行合成，增强森林覆盖区和背景区信息的反差，并利用最大似然法对影像进行监督分类，分类精度超过92％。通过对不同云雾量和森林覆盖的2个时相影像试验表明，本方法提高了遥感影像森林覆盖信息提取的自动化程度和精度。
With the Daxinganling area as the experimental site, the authors used the PCA （Principal Components Analysis） to get the first weight and the results of the NDVI ployed a certain band in the LandSat TM1 to 7 to realize the the contrast between the forest information and the background （Normalized Difference Vegetation Index） , and emcombination of the wave band, which could enhance information. The study also used the method of maximum likelihood to realize the supervised classification of the images, whose accuracy could exceed 92%. This paper presents two experimental investigation images with different quantities of cloud and different extents of forest coverage. This investigation shows that this method can improve the automation and precision in the extraction of the forest coverage information.
出处
《国土资源遥感》
CSCD
2007年第2期82-85,共4页
Remote Sensing for Land & Resources
基金
国家科技基础条件平台工作重点项目"森林灾害的监测
预警与管理系统平台(2003DIA6N007)"
关键词
主成分分析
NDVI
波段组合
监督分类
邻域分析
最大似然法
Principal components analysis
Normalized difference vegetation index
Band combinations
Supervised classification
Neighborhood analysis
Maximum likelihood
