基于梯度提升决策树的植被高度模型研究

Study on vegetation height model based on the gradient boosting decision tree

【目的】研究以航空摄影测量的方式建立植被高度模型。【方法】利用数字正射影像(DOM)与数字表面模型(DSM)提取光谱特征因子和几何特征因子,采用相关性指数对植被高度与特征因子进行相关性分析,筛选出特征因子。采用梯度提升决策树算法建立植被高度模型,并通过优化参数提高模型精度。【结果】在默认参数下,模型精度约为2.000 m;优化参数后,模型精度达到了0.900 m;剔除部分特征因子后,模型精度可达0.840 m;通过与支持向量机算法进行对比,植被高度模型整体精度由0.893 m提高至0.758 m,运行时间由70 min缩减至10 min。【结论】若不考虑建模原始数据的误差,采用梯度提升决策树算法建立的植被高度模型的精度为亚米级,多次试验中模型精度较为稳定。

[Purposes]The study aims to establish vegetation height model by aerial photogrammetry.[Methods]Based on digital orthophoto map and digital surface model,spectral and geometric feature factors were extracted for vegetation height modeling.The correlation between vegetation height and feature factor was analyzed by correlation index,the feature factors were selected.The gradient boosting decision tree algorithm was adopted to establish the vegetation height prediction model,and the accuracy of the model was improved through parameter optimization.[Findings]The model accuracy is about 2.000 m under the default parameter.By optimizing the parameters,the model accuracy reaches 0.900 m.Furthermore,the model accuracy enhance to 0.840 m,resulted from excluding some feature factors.By compared with the support vector machine algorithm,the accuracy of the vegetation height model has been increased from 0.893 m to 0.758 m,and the running time has been reduced from 70 minutes to 10 minutes.[Conclusions]The accuracy of the vegetation height model can reach to sub meter,when the error of the original modeling data is neglected,and the accuracy of the model remains stable in many experiments.