Decision tree classification of remote sensing images based on vegetation indices
一种基于植被指数的遥感影像决策树分类方法

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.