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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (3) : 20-28     DOI: 10.6046/gtzyyg.2019.03.03
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A review of research on remote sensing for ground impervious surface percentage retrieval
Jiaqi ZUO1,2, Zegen WANG1, Jinhu BIAN2(), Ainong LI2, Guangbin LEI2, Zhengjian ZHANG2
1. School of Civil Engineering and Architecture, Southwest Petroleum University, Chengdu 610500, China
2. Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
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Abstract  

Urban expansion caused by urbanization brings many problems to the social and ecological environment. Monitoring urban change is an entry point to solve these problems. As an important indicator of urban expansion, the impervious surface has become a hotspot of research. The acquisition of impervious surface data and its time series variation analysis are the core of the current study. Compared with early planar map-based impervious surface extraction, remote sensing has been widely used in impervious surface research due to its continuous, rapid and extensive observation of the ground. Multi-source data fusion and multiple retrieval method make constant progress in remote sensing-based retrieval of ground impervious surface percentage, and the focus of the study is gradually shifted from the classification map of impervious surface to the quantitative retrieval of impervious surface percentage. In this paper, the authors summarized the methods of remote sensing retrieval of sub-pixel impervious surface percentage from the perspectives of single period and time series, analyzed the advantages and disadvantages of the retrieval methods in detail, and briefly described and compared the common precision validation method. Finally, the authors summarized the problems existing in the current remote sensing retrieval methods, proposed the corresponding solutions, and pointed out the trend of development in the future.
Keywords impervious surface percentage      quantitative remote sensing retrieval      time series      urbanization     
:  TP79  
Corresponding Authors: Jinhu BIAN     E-mail: bianjinhu@imde.ac.cn
Issue Date: 30 August 2019
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Jiaqi ZUO
Zegen WANG
Jinhu BIAN
Ainong LI
Guangbin LEI
Zhengjian ZHANG
Cite this article:   
Jiaqi ZUO,Zegen WANG,Jinhu BIAN, et al. A review of research on remote sensing for ground impervious surface percentage retrieval[J]. Remote Sensing for Land & Resources, 2019, 31(3): 20-28.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.03.03     OR     https://www.gtzyyg.com/EN/Y2019/V31/I3/20
Fig.1  Feature distribution of the first three MNF components (adapted from Cheng et al.[10])
模型 On b1-7 Off b1-7 On TC Off TC DEM Light Slope AE/% RE/% R
CART1 √ √ 13.5 0.41 0.84
CART2 √ √ √ √ √ √ √ 13.6 0.42 0.84
CART3 √ √ √ √ √ √ 12.7 0.39 0.86
CART4 √ √ √ 12.8 0.39 0.86
CART5 √ √ 13.6 0.41 0.84
CART6 √ √ 15.2 0.46 0.81
Tab.1  CART models and corresponding input variables
模型 模型机理 必要参数 模型优点 区域 反演精度
决策树[36] 一个二分递归的树结构模型。算法根据训练数据产生的规则将输入样本集分成多类或连续的目标变量,由每个规则定义多变量线性回归模型建立的条件 叶节点所需最小样本数、树的最大深度 不需输入数据满足统计分布要求,将输入变量和目标变量的关系简化为多元变量的线性关系 北京市 AE=12.7%
RE=0.39
R=0.86
SVM[42] 基于统计学习理论和结构风险最小原理的二分类模型,其基本思想是求解能够正确划分训练数据集且几何间隔最大的分离超平面 核函数类型、核参数、惩罚因子 具有较好的泛化能力,对较少的训练样本和高空间维度的输入数据具有较强的鲁棒性 柏林市 MAE=12.4%
R2=0.52
MLP[43] 包含输入层、输出层和隐藏层的网络,整个网络是通过一个调整节点间互连权重强度的迭代学习过程来建立预测模型 网络架构、学习算法、训练迭代的次数 能够逼近任意的非线性函数,可以处理系统内难以解析的规律性,学习收敛速度快 印第安纳州波利斯市 RMSE=12.3%
R2=0.77
RF[14] 改良版的袋装回归树。其每一次分裂生长过程中对输入变量的选择都是随机的,且在分裂过程中允许输入其他变量。算法将对节点上的变量和输入变量进行比较,选择影响最大的变量进行分裂生长 训练样本的大小、树的棵数、每个节点选取的随机变量个数 泛化误差有限,不会因为树木棵数的增大而出现过拟合的情况 武汉市 总体精度为
97.06%
Tab.2  Comparison of machine learning retrieval models of impervious surface
Fig.2  Simulating the ISP by using Logistic function(adapted from Song et al.[48])
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