A Novel Sample Selection Method for Impervious Surface Area Mapping Using JL1-3B Nighttime Light and Sentinel-2 Imagery

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Shanchuan Guo¹  Pengfei Tang¹  Cong Lin¹  Peijun Du¹  Lu Qie² 

[1] Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, Nanjing University, Nanjing, China;Ministry of Natural Resources, Key Laboratory of Coastal Zone Exploitation and Protection, Nanjing University, Nanjing, China;

关键词: Automatic sampling;    high-resolution nighttime light imagery (NTL);    impervious surface area (ISA);    Sentinel-2 imagery;    urban remote sensing (RS);   

DOI  :  10.1109/JSTARS.2020.3004654

来源: DOAJ

【 摘 要 】

Urbanization has attracted wide and active interests due to the impact on regional sustainable development. As an important indicator of urbanization, impervious surface area (ISA) should be accurately monitored. In scenario of identifying ISA by supervised classification from satellite images, the training samples are usually labeled manually, which is highly labor-intensive and time-consuming. High-resolution nighttime light image provides a unique footprint of human activities and settlements which are strongly correlated with ISA. In view of this, a novel ISA training sample selection method is proposed by integrating the JL1-3B high-resolution nighttime light imagery and Sentinel-2 time series imagery, and the random forest is applied to classify ISA from Sentinel-2 imagery. The quality of the automatically selected samples was quantitatively validated. There were over three study areas, and the overall classification accuracies were above 97%, showing reliable and robust performance. Compared with conventional methods, the proposed approach achieves satisfactory results in separating bare land from ISA. This study provides a data fusion way which can automatically generate sufficient and high-quality training samples for ISA mapping, and suggests that high-resolution nighttime imagery could demonstrate a promising potential for urban remote sensing.

【 授权许可】

Unknown