一种基于植被指数的遥感影像决策树分类方法

以江苏省徐州市为研究区，采用2000年ETM+多光谱影像作为遥感信息源，选择影像的光谱特征和归一化植被指数（NDVI）、绿度植被指数（GVI）、比值植被指数（RVI）等10种植被指数作为分类特征，基于See5决策树学习软件构建分类决策树，实现了研究区景观格局的遥感分类。研究结果表明，决策树分类法易于综合多种特征进行遥感影像的分类，植被指数参与到决策树分类中能够提高分类的总体精度。

Decision tree classification of remote sensing images based on vegetation indices

In order to explore the applications of ETM+ remote sensing data to urban landscape pattern analysis, the decision tree classifier based on See5 was developed and its generation strategy was discussed in detail. Taking Xuzhou city as the study area, spectral features and ten vegetation indices, including Normalized Difference Vegetation Index (NDVI), Greenness Vegetation Index (GVI), Ratio Vegetation Index (RVI) and so on, were used and extracted for decision tree classification. By comparing the classification results with decision tree classifier based on spectral features only, vegetation indices used in the processing of remote sensing images classification could advance the classification accuracy. The result also shows that the decision tree classifier is effective to landscape pattern classification from remote sensing images based on various features.