基于C5.0的遥感影像决策树分类实验研究

决策树算法是一种非参数化、非线性的监督分类法。以2010年8月1日Landsat TM影像为基础遥感信息源,以内蒙古自治区赤峰市中部巴林右旗、林西县、克什克腾旗、翁牛特旗交汇处的区域为研究区,通过多次修改完善训练样本数据集,然后把6个原始波段和NDVI、主成分分析后的前3个主分量、常用8个纹理特征以及3个地形特征等共21个特征变量组合成5个不同特征变量组合,采用典型决策树算法C5.0进行了遥感影像分类实验,与最大似然分类结果进行对比。结果表明:C5.0决策树的分类结果优于最大似然结果,尤其是特征变量组合恰当的时候,能够有效利用相关辅助信息,因而最终的分类结果更能满足用户需求。

The Study of the Remote Sensing Image Classification based on C5.0 Algorithm of Decision Tree

The decision tree algorithm is a non\|parametric,nonlinear supervised classification method.This study takes Landsat TM images of August 1,2010 as the basic remote sensing information source,choosing the intersection of Bairin Right County,Linxi County,Hexigten County and Wengniute County,the center region of Chifeng City,Inner Mongolia,China as the study area and by repeatedly modified to improve the training data set,then select twenty\|one characteristic variables combinations whicht consist of five different characteristic variables combinations,and they include six original bands and the NDVI based on them,principal components (PC1,PC2 and PC3),eight texture features (Mean,Variance,Homogeneity,Contrast,Dissimilarity,Entropy and the Second Moment and the Correlation) and three topographical features (DEM,Slope and Aspect),then this study utilizes the typical decision tree algorithm C5.0 to classify them,and the final results were compared with that of maximum likelihood classification results.The result shows that the decision tree classification result is better than maximum likelihood classification result ,especially,when the combinations of characteristic variables are appropriate,which is able to effectively use the related auxiliary information,then their final classification results are more satisfy the users demand.