Application of random forest and Sentinel-1/2 in the information extraction of impervious layers in Dongying City

doi:10.6046/zrzyyg.2020310

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Abstract

An impervious layer is an important indicator of human activities. Timely and accurate information of impervious layers is of great significance for the protection of the ecological environment. Taking the Yellow River Delta (Dongying City) as the study area, this study explores a novel extraction method of impervious layers by combining the random forest classification with Sentinel-1/2 data. According to comparative experiments, the confusion between dark and light impervious layers and bare soil can be reduced through the combination of the random forest algorithm with surface reflectance characteristics, texture characteristics, and backscatter coefficient, thus effectively improving the estimation accuracy of impervious layers (overall accuracy: 93.37%, Kappa coefficient: 0.925 8). The results of this study reveal that the random forest algorithm combined with Sentinel-1/2 data is a promising approach in the information extraction of impervious layers, which will provide a reference for the remote sensing monitoring of the Yellow River Delta through the integration of multi-source data.

Keywords Dongying City impervious layer Sentinel-2 Sentinel-1 texture random forest

ZTFLH:

TP79

Corresponding Authors: LIU Jiantao E-mail: ctliu96@163.com;liujiantao18@sdjzu.edu.cn

Issue Date: 24 September 2021

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	Chunting LIU
	Quanlong FENG
	Dingjian JIN
	Tongguang SHI
	Jiantao LIU
	Mingshui ZHU

Cite this article:

Chunting LIU,Quanlong FENG,Dingjian JIN, et al. Application of random forest and Sentinel-1/2 in the information extraction of impervious layers in Dongying City[J]. Remote Sensing for Natural Resources, 2021, 33(3): 253-261.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2020310 OR https://www.gtzyyg.com/EN/Y2021/V33/I3/253

Fig.1 Image of Sentinel-2 B4(R),B3(G),B2(B) bands in the study area

Tab.1 Band information of Sentinel-2

Fig.2 Flowchart of research

Tab.2 Classification scheme and number of samples(个)

Fig.3 Distribution of sampling points

Tab.3 Confusion matrices for classification

Fig.4 Extracted result of impervious surface

Tab.4 Combination schemes of Sentinel data

Tab.5 Statistics of classification accuracy

Fig.5 Classification results of different schemes

Tab.6 Comparison of classification accuracy of RF, SVM and CART

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