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