基于随机森林模型的城市不透水面提取研究——以呼和浩特市为例

Research on extraction of urban impervious surface based on random forest model: a case study in Hohhot

城市不透水面信息对于城市生态环境动态演化过程研究具有重要意义。以Landsat 8遥感影像为数据源,以呼和浩特市为实证区域,进行了随机森林模型应用于城市不透水面的提取研究,并与目前应用广泛的支持向量机模型进行了对比分析。研究表明:在不同的抽样比例训练样本条件下,随机森林模型对于城市不透水面的提取精度均优于支持向量机的提取精度;对于随机森林模型和支持向量机模型,70%的训练样本比例均为最佳训练样本抽样比例。在该抽样比例下,随机森林模型提取城市不透水面的总体分类精度为93. 29%,Kappa系数为0. 9051,支持向量机模型的总体分类精度为91. 26%,Kappa系数为0. 8757;随机森林模型对于城市裸土的识别度较高,能更好地将城市裸土和不透水面进行区分,而支持向量机模型对于城市裸土、不透水面和绿地的区分能力均弱于随机森林模型。综合而言,随机森林模型对城市不透水面的提取精度优于支持向量机模型,随机森林模型可以有效应用于城市不透水面提取领域,进一步丰富了城市不透水面提取方法体系构成。

Urban impervious surface information is essential for the research of dynamic evolution of urban ecological environment. In this paper,based on Landsat 8 remote sensing image,taking Hohhot city as a case,the urban impervious surface has been extracted by using random forest model and compared with the w idely applicable support vector machine model. The research results show ed that（ 1） the accuracy of the extraction of urban impervious surface by random forest model is better than that of the support vector machine model under the condition of various proportion of sampling training samples;（ 2） 70% of the proportion of training sample is the most optimal for either random forest model or support vector machine model. On the basis of the most optimal proportion of training sample,the overall classification accuracy of extraction of urban impervious surface with random forest model is 93. 29% with a Kappa coefficient of 0. 9051. The classification accuracy of the support vector machine model is 91. 26% with a Kappa coefficient of 0. 8757;（ 3） the random forest model has a more sensitive recognition to urban bare soil,and can better distinguish the urban bare soil from the impervious surface,w hile the support vector machine model cannot accurately distinguish the bare soil,impermeable surface and the green space. In short,the random forest model can more effectively and accurately extract urban impervious surface from Landsat 8 image and be applied in the field of extracting urban impervious surface,enriching the method system of extracting urban impervious surface.