地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (10): 1399-1409.doi: 10.3724/SP.J.1047.2016.01399

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基于Cubist模型树的城市不透水面百分比遥感估算模型

戴舒(), 付迎春*(), 赵耀龙   

  1. 华南师范大学地理科学学院 广东省智慧国土工程技术研究中心, 广州 510631
  • 收稿日期:2015-09-14 修回日期:2015-12-20 出版日期:2016-10-25 发布日期:2016-10-25
  • 通讯作者: 付迎春 E-mail:daishusysu@foxmail.com;fuyc@scnu.edu.cn
  • 作者简介:

    作者简介:戴 舒(1990-),男,江西抚州人,硕士生,主要从事城市信息处理与定量遥感研究。E-mail: daishusysu@foxmail.com

  • 基金资助:
    国家自然科学基金项目(41101152);“973”计划前期研究专项(2014CB460614);广州市产学研协同创新重大专项民生科技项目(156100021);广东省科技计划项目(2015A010103013)

The Remote Sensing Model for Estimating Urban Impervious Surface Percentage Based on the Cubist Model Tree

DAI Shu(), FU Yingchun*(), ZHAO Yaolong   

  1. School of Geography, South China Normal University, Guangdong Provincial Center for Smart Land Research, Guangzhou 510631, China
  • Received:2015-09-14 Revised:2015-12-20 Online:2016-10-25 Published:2016-10-25
  • Contact: FU Yingchun E-mail:daishusysu@foxmail.com;fuyc@scnu.edu.cn

摘要:

不透水面是城市区域中一种典型的土地覆盖类型,是衡量城市环境质量和城市化水平的重要标志之一。与传统基于像元级的遥感研究方法相比,不透水面百分比(Impervious Surface Percent,ISP)的估算可以进入像元内部,获得更准确的城市信息。本文应用Cubist模型树,对Landsat TM的原始波段变量(除热红外波段),建立ISP估算的基础模型(Base Cubist-ISP)。通过基于模型树的集成学习优化算法和加入相邻时相影像的波段变量中值,以削弱噪声的影响。然后,优选热红外波段和各种衍生变量,并进行属性精简,继而应用集成学习算法得到的参数和精简后的变量建立ISP估算的优化模型(Optimal Cubist-ISP)。对广东省广州市海珠区的实验结果表明,Optimal Cubist-ISP模型估算不透水面的整体均方根误差(RMSE)为12.98%,决定系数(R2)为0.90,精度明显优于Base Cubist-ISP模型,RMSE降低约5.03%,ISP在透水面区域被高估和高密度不透水面区域被低估的现象得到改善。本文提出的基于Cubist模型树建立ISP遥感估算的模型及优化方法可以适用于城市区ISP的提取。

关键词: 不透水面, Cubist模型树, 集成学习算法, Optimal Cubist-ISP模型

Abstract:

As a typical land-cover type, impervious surface is a key indicator of urban environmental quality and urbanization scope. In comparison with the traditional remote sensing image processing methods, the assessment of impervious surface percentage (ISP) can offer the sub-pixel level exploration and acquire the fine-scale information. In this paper, the proposed method uses the Cubist model tree with both the high-resolution (Google Earth) and the medium-resolution (Landsat TM/ETM+) remote sensing data to establish an estimation model of impervious surface percentage (ISP). A base model (Base Cubist-ISP) is built integrating all the original bands from Landsat TM excluding the thermal infrared band. This paper tries to minimize the effects of noise by adopting the ensemble learning algorithm and by incorporating the median of each solar-reflective band within the adjacent temporal images. After that, the following variables are filtered to get the optimized results, including the TM thermal infrared band, the derived variables from the original bands such as Texture, and the tasseled cap transformation variables. Then the variables are simplified, and in that way, the optimized parameter of ensemble learning algorithm for Cubist tree and the well-chosen variables are used to establish an optimization estimation model (Optimal Cubist-ISP). The results of a case study for Haizhu district, which is located in Guangzhou city of Guangdong Province, show that the overall root mean square error between the estimated ISP value, which is based on the Optimal Cubist-ISP model, and the reference ISP value is 12.98%, with a determinant coefficient of 0.90. Moreover, this paper compares the Base Cubist-ISP model with the Optimal Cubist-ISP model. The accuracy of the Optimal Cubist-ISP model is better than the Base Cubist-ISP model, and the RMSE decreases by about 5.03%. It is illustrated that the Base Cubist-ISP model may over-estimate the pervious surface area and under-estimate the high density impervious surface area, which could be improved by the model optimization. In addition, the Optimal Cubist-ISP model can not only be able to well recognize the land types of soil and water, but also eliminate the influence of shadow on the high density building area to a certain extent. Thus, the proposed approach on impervious surface estimation based on the Cubist model tree as well as its optimization scheme can be applied for precisely obtaining the ISP in the urban areas.

Key words: impervious surface, Cubist model tree, ensemble learning algorithm, Optimal Cubist-ISP model