Abstract
Impervious surface data has become an indicator of the degree of urbanization and the environmental quality, which inspires the widely use of remote sensing technology in extracting impervious surface. In order to reduce the confusion between impervious surface and other landcover types, this paper proposed an effective urban impervious surface extraction method based on multi-features and Random Forest (RF). First, Sentinel-2 multispectral data and Luojia 1–01 images are employed to extracted multi-features, including spectral features, texture features and temporal features. Then, a feature selection method with null importance is proposed to remove irrelevant features. Finally, Probabilistic Label Relaxation (PLR) is introduced into RF to obtain the classification results and impervious surface. The main urban areas of Zhengzhou and Hangzhou are selected as study areas. The experiment results show that the integration of the multi-features can significantly improve the overall accuracy of classification and the extraction accuracy of impervious surface. And the classification accuracy can be further improved after the feature selection with null importance. Besides, the PLR method can effectively reduce the salt-and-pepper phenomenon of classification results using random forest, which presupposes the optimal number of iterations. The method proposed in this paper can effectively improve the estimation of impervious surface and provide an important reference for the extraction of impervious surface based on pixel level.
Highlights
Impervious surface is natural or artificial surface that prevents surface water from penetrating into the soil and thereby altering the flow of flood runoff, such as roads, car parks and rooftops made of cement concrete, glass, asphalt, plastics, tiles, metals, etc
High-precision impervious surface information is of great scientific significance to the sustainable development of cities, and attracts unprecedented attention
In order to overcome this difficulty and obtain a high-precision impervious surface estimation, this paper proposed the method of combining multi-features and neighborhood-supported classification
Summary
Impervious surface is natural or artificial surface that prevents surface water from penetrating into the soil and thereby altering the flow of flood runoff, such as roads, car parks and rooftops made of cement concrete, glass, asphalt, plastics, tiles, metals, etc. High-precision impervious surface estimation is of great significance for environmental management, urban planning, urban construction, hydrological environment and economic development[1,2,3,4,5,6,7]. Remote sensing technology has become the main technique for impervious surface mapping due to its large monitoring range, fast acquisition speed and low cost. A variety of methods for extracting impervious surface using remote sensing data have been successively proposed and successfully applied to urban planning, environmental monitoring, water resource management and other fields, which are mainly divided into sub-pixel methods [8,9,10] and traditional per-pixel methods[11,12,13]. The spectral confusion is ineluctable due to the heterogeneity of urban landscape and the spectral resolution of images, the confusion between impervious surface and bare land
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