作　　者： ; ; ; ; ; ;

机构地区： 吉林大学地球探测科学与技术学院

出　　处： 《吉林大学学报（信息科学版）》 2013年第1期83-89,共7页

摘　　要： 为提高植被分类的精度,在利用高光谱图像提取植被信息时需要考虑训练样本和地形等其他因素的影响。以长白山为研究背景,基于CART(Classification And Regression Tree)算法构建决策树模型,对高光谱图像进行植被分类。由于混合像元的影响,以采用PPI(Pixel Purity Index)提取的纯净像元作为训练样本,提取植被指数、纹理和地形等分类特征变量。基于这些变量构建CART决策树对植被分类,并将结果与最大似然法分类结果进行比较。结果表明,CART决策树分类法可实现光谱、纹理和地形特征的有效组合,有较好的分类效果。 To improve the accuracy of vegetation classification, the influences of training sample sizes and terrain should be considered when extracting vegetation information from the hyperspectral image. Taking the Changbai Mountain as the study area, this paper built a decision tree model based on CART （ Classification and Regression Tree） algorithm to classify vegetation in hyperspectral image. In order to reduce the influence of the mixed pixels, using PPI （Pixel Purity Index） to extract pure pixel as the training samples. CART decision tree was built based on these classification feature variables, such as vegetation index, texture, terrain and so on, the tree was applied on vegetation classification and the result was compared with the maximum likelihood classification. The result showed that CART decision tree method combined with spectrum, texture and terrain, had a better effect of classification.

关 键 词： 高光谱 植被分类 端元提取 决策树

领　　域： [自动化与计算机技术] [自动化与计算机技术]