Journal of Geo-information Science ›› 2019, Vol. 21 ›› Issue (9): 1420-1429.doi: 10.12082/dqxxkx.2019.180679

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Extraction of Urban Impervious Surface from High-Resolution Remote Sensing Imagery based on Deep Learning

CAI Bowen1,WANG Shugen1,WANG Lei2,SHAO Zhenfeng2,*()   

  1. 1. School of Remote Sensing and Information Engineering,Wuhan University ,Wuhan 430079, China
    2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
  • Received:2018-12-30 Revised:2019-06-12 Online:2019-09-25 Published:2019-09-24
  • Contact: SHAO Zhenfeng E-mail:shaozhenfeng@whu.edu.cn
  • Supported by:
    National Key R&D Program Projects(2016YFE0202300);National Natural Science Foundation of China(61671332);National Natural Science Foundation of China(41771452);National Natural Science Foundation of China(41771454);National Natural Science Foundation of China(51708426);National Natural Science Foundation of China(41890820);National Natural Science Foundation of China(41771454);Hubei Natural Science Foundation Program Innovation Group Project(2018CFA007)

Abstract:

Impervious surface is an important indicator of urban ecological environment, which is of great significance for urbanization and environmental quality assessment. The complexity of urban land use and the diversity of impervious surface materials make it a challenge to extract impervious surface directly from high-resolution remote sensing imagery. To meet the requirement of impervious surface extraction from high-resolution remote sensing imagery at the urban scale, a model of impervious surface extraction based on deep learning was proposed in this paper. Firstly, deep convolution neural network was used to extract image features. In extracting impervious surface of complex cities, a convolution layer and a pool layer were retained. While the void convolution was introduced to increase the field of receptivity and reduce the loss of information, so that each convolution output contained a larger range of information. Secondly, a probabilistic graph learning model was constructed according to its neighborhood relationship, and high-order semantic information was introduced to optimize the features to achieve accurate extraction of impervious surfaces. This paper choosed Wuhan as the experimental area, and took GaoFen-2 satellite remote sensing imagery as the data source to implement the proposed model for the extraction of impervious surface thematic information. The automatic extraction accuracy was 89.02% in the construction area and 95.55% in the urban-rural junction. Compared with the traditional machine learning algorithms such as random forest and support vector machine, the efficiency and accuracy of the proposed deep learning method were better. Statistics and analysis of the impervious surface information of the main administrative regions in Wuhan showed that the proportion of impervious surface in the whole territory of Wuhan was 11.43%, and the proportion of impervious surface in the core main urban area was close to 70%. Additionally, the present situation and development planning characteristics of Wuhan were analyzed and discussed. The impervious surface can be used as a link between urban development level and environmental quality. The distribution of impervious surface in Wuhan development planning of various administrative districts is closely related to the sustainable development of the city. Our findings suggest that the deep learning method is effective for the extraction of impervious surfaces from high-resolution remote sensing imagery, and can provide technical support and data reference for the construction of sponge city and ecological city.

Key words: impervious surface, deep learning, high resolution remote sensing images, convolutional neural network, probabilistic graph model, Wuhan