基于CART集成学习的城市不透水层百分比遥感估算
Estimating Urban Impervious Surface Percent Using Boosting as a Refinement of CART Analysis
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摘要: 利用Landsat ETM+遥感数据,提出了一种基于CART集成学习的ISP遥感亚像元估算方法,将Boosting重采样技术引入CART分析中,用于提高ISP估算的精度。实验结果表明,该方法的ISP估算性能优于传统的单一CART学习算法,从ETM+影像中估算的ISP值与真实值之间的相关系数达到0.91,平均偏差为11.16%。Abstract: An approach for estimating urban imperviousness surface percent(ISP) through the synergistic use of Landsat ETM+ and high-resolution imagery is presented.In the proposed approach,the ensemble leaning of CART analysis based on Boosting is utilized to construct the ISP predict model,furthermore,to obtain the subpixel ISP results at 30 m resolution.The experiment shows this approach yields ISP estimation performance compared to that of the traditional estimation method based on single CART,and its correlation coefficient of predicted versus actual ISP reaches 0.91 with an average error 11.16%.
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