Research on Remote Sensing Classification of Urban Vegetation Species Based on SVM Decision-making Tree
基于SVM决策支持树的城市植被类型遥感分类研究

Different vegetation species have different biological quality and produce different ecological functions and greenery effect.Considering the "Vegetation Quality" is difficult to be obtained,the biological quality and ecological effect of urban greenery can be indirectly reflected using urban green-land area and vegetation species.Based on the comparison of the traditional statistic parameter and non-parameter classification methods and the analysis of kernel-function of SVM,SVM decision-making tree model for urban vegetation classification is designed in this paper using the high resolution imagery data of IKONOS.The classification results are compared to other traditional methods and have an average vegetation classification accuracy of about 83.5% and green-land area accuracy nearly 95%.