TY - JOUR
T1 - New Scheme for Impervious Surface Area Mapping from SAR Images with Auxiliary User-Generated Content
AU - Wu, Wen
AU - Miao, Zelang
AU - Xiao, Yuelong
AU - Li, Zhongbin
AU - Zhang, Anshu
AU - Samat, Alim
AU - Du, Nianchun
AU - Xu, Zhuokui
AU - Gamba, Paolo
N1 - Funding Information:
Manuscript received July 3, 2020; revised September 7, 2020; accepted September 25, 2020. Date of publication September 29, 2020; date of current version October 12, 2020. This work was supported in part by the State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences under Grant G2019-02-06, in part by the National Natural Science Foundation of China under Grant 41701500, in part by the Natural Science Foundation of Hunan Province under Grant 2018JJ3641 and Grant 2019JJ60001, in part by the Scientific Research Fund of Hunan Provincial Education Department under Grant 13B129, in part by the Hong Kong RGC under Grant PolyU 152201/17E, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20190785, in part by the Innovation-Driven Project of Central South University under Grant 2020CX036, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20190785, and in part by the Postgraduate Innovation Project of Central South University under Grant 2020ZZTS693. (Wen Wu, Zelang Miao, Yuelong Xiao, and Zhongbin Li contributed equally to this work.) (Corresponding author: Zelang Miao.) Wen Wu, Zelang Miao, and Yuelong Xiao are with the School of Geoscience and Info-Physics, Central South University, Changsha 410083, China, and also with the the Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Chang-sha 410083, China (e-mail: vennww@csu.edu.cn; zelang.miao@outlook.com; 1065021381@qq.com).
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - This article presents a new scheme to extract impervious surface area from synthetic-aperture radar (SAR) images exploiting auxiliary user-generated content (UGC). The presented scheme includes the automatic generation of training samples based on the combination of UGC and SAR data, and SAR data preprocessing, leading to impervious surface area classification through a clustering-based one-class support vector machine approach. Two areas-namely, the cities of Beijing and Taipei, have been analyzed using the Sentinel-1 SAR data to test and validate the proposed methodology. Experimental results show that the presented scheme improves the automatic selection of impervious surface training samples. Moreover, this scheme achieves a comparable classification performance to traditional methods without requiring time-consuming training point manual extraction. Results in this study will help to promote the application of UGC for urban remote sensing data interpretation.
AB - This article presents a new scheme to extract impervious surface area from synthetic-aperture radar (SAR) images exploiting auxiliary user-generated content (UGC). The presented scheme includes the automatic generation of training samples based on the combination of UGC and SAR data, and SAR data preprocessing, leading to impervious surface area classification through a clustering-based one-class support vector machine approach. Two areas-namely, the cities of Beijing and Taipei, have been analyzed using the Sentinel-1 SAR data to test and validate the proposed methodology. Experimental results show that the presented scheme improves the automatic selection of impervious surface training samples. Moreover, this scheme achieves a comparable classification performance to traditional methods without requiring time-consuming training point manual extraction. Results in this study will help to promote the application of UGC for urban remote sensing data interpretation.
KW - Clustering-based one-class support vector machine
KW - impervious surface area (ISA)
KW - synthetic-aperture radar (SAR)
KW - user-generated content (UGC)
UR - http://www.scopus.com/inward/record.url?scp=85093954490&partnerID=8YFLogxK
U2 - 10.1109/JSTARS.2020.3027507
DO - 10.1109/JSTARS.2020.3027507
M3 - Journal article
AN - SCOPUS:85093954490
VL - 13
SP - 5954
EP - 5970
JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
SN - 1939-1404
M1 - 9209147
ER -