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Estimating Impervious Surface Distribution: A Comparison of Object Based Analysis and Spectral Mixture Analysis

机译：估计不透水的表面分布：基于对象的分析和光谱混合分析的比较

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Knowledge about the distribution of impervious surface is important to the understanding of urbanization processes and environmental consequences. This paper compares two methods for classifying the degree of imperviousness for the large metropolitan region of the fast growing city of Guangzhou in southern China: spectral mixture analysis (SMA) and object based image analysis (OBIA). Both methods were applied to data from the Chinese Satellite Huan Jing 1-B (HJ-1B). Samples were classified using a Support Vector Machine (SVM), ultimately yielding the percentages of Impervious Surface Areas (ISA). The estimation accuracy for ISA was assessed using the land surface temperature retrieval result based on HJ-1B satellite data. In general, OBIA achieved slightly better results, while both methods achieved low accuracies in the highly vegetated northern and eastern parts of the study area. Our results show that OBIA combined with SVM classification of the HJ-1B data provides a means to map and quantify the degree of impervious surface distribution.
机译：关于不透水表面分布的知识对于了解城市化进程和环境后果非常重要。本文比较了两种对中国南部广州快速发展城市大都市区的不渗透度的方法：光谱混合分析（SMA）和基于物体的图像分析（OBIA）。两种方法都应用于中国卫星桓静1-B（HJ-1B）的数据。使用支持向量机（SVM）分类样品，最终产生不透水表面积（ISA）的百分比。使用基于HJ-1B卫星数据的土地表面温度检索结果评估ISA的估计准确度。一般来说，OBIA达到了稍微更好的结果，而两种方法在研究区的高度植被北部和东部地区的高精度方面取得了低精度。我们的结果表明，OBIA与HJ-1B数据的SVM分类相结合，提供了一种映射和量化不透水表面分布的方法。

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