Paper Info
Title
Change Of Impervious Surface Of Chengdu City, China
Abstract
Impervious surfaces have become the most intuitive indicator in the process of urbanization. Timely and accurate information on impervious surfaces from remote sensing images is essential. It not only helps us understand the process of land use/cover change, but also the influences on human society and the environment. In this study, convolutional neural network (CNN) was used to extract the impervious surface in Chengdu city, Sichuan province, China. The overall accuracy in 2009 and 2017 were 98.75% and 99.76% respectively. From the results for 2009 and 2017, the impervious surface increased by 51.24 km(2), Growth rate is 13.8%. During the process of urban expansion, suburban farmland was replaced by impervious surfaces and the area of impervious surface gradually increased.
Year
DOI
Venue
2020
10.1109/IGARSS39084.2020.9323686
IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
Keywords
DocType
Citations 
Classification, Convolutional neural network (CNN), Urbanization, Impervious surface
Conference
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Yue He110516.62
Xiaobo Zhang200.68
Jibao Shi300.68
Jun Xia4227.46
Kai Chen500.68
Tao Weng600.68
Zezhong Zheng72912.43