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Remote Sens. 2016, 8(11), 945; doi:10.3390/rs8110945

Mapping Urban Impervious Surface by Fusing Optical and SAR Data at the Decision Level

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
2
Chinese Academy of Surveying and Mapping, Lianhuachixi Road 28, Haidian District, Beijing 100830, China
3
Center for Urban and Environmental Change, Department of Earth and Environmental Systems, Indiana State University, Terre Haute, IN 47809, USA
4
Department of Urban and Regional Planning, University at Buffalo, The State University of New York, Buffalo, NY 14214, USA
*
Author to whom correspondence should be addressed.
Academic Editors: James Campbell, Xiaofeng Li and Prasad S. Thenkabail
Received: 31 July 2016 / Revised: 30 October 2016 / Accepted: 7 November 2016 / Published: 12 November 2016
(This article belongs to the Special Issue Monitoring of Land Changes)
View Full-Text   |   Download PDF [23653 KB, uploaded 12 November 2016]   |  

Abstract

The proliferation of impervious surfaces results in a series of environmental issues, such as the decrease of vegetated areas and the aggravation of the urban heat island effects. The mapping of impervious surface and its spatial distributions is of significance for the ecological study of urban environment. Currently, the integration of optical and synthetic aperture radar (SAR) data has shown advantages in accurately characterizing impervious surface. However, the fusion mainly occurs at the pixel and feature levels which are subject to influences of data noises and feature selections, respectively. In this paper, an innovative and effective method was developed to extract urban impervious surface by synergistically utilizing optical and SAR images at the decision level. The objective of this paper was to obtain an accurate urban impervious surface map based on the random forest classifier and the evidence theory and to provide a detailed uncertainty analysis accompanying the fused impervious surface maps. In this study, both the GaoFen (GF-1) and Sentinel-1A imagery were first used as independent data sources for mapping urban impervious surfaces. Then additional spectral features and texture features were extracted and integrated with the original GF-1 and Sentinel-1A images in generating impervious surfaces. Finally, based on the Dempster-Shafer (D-S) theory, impervious surfaces were produced by fusing the previously estimated impervious surfaces from different datasets at the decision level. Results showed that impervious surfaces estimated from the combined use of original images and features yielded a higher accuracy than those from the original optical or SAR data. Further validations suggested that optical data was better than SAR data in separating impervious surfaces from non-impervious surfaces. The fused impervious surfaces at the decision level had a higher overall accuracy than those produced independently by optical or SAR data. It was also highlighted that the fusion of GF-1 and Sentinel-1A images reduced the amount of confusions among the low reflectance of impervious surface and water, as well as for low reflectance of bare land. An overall accuracy of 95.33% was achieved for extracting urban impervious surfaces by fused datasets. The spatial distributions of uncertainties provided by the evidence theory displayed a confidence level of at least 75% for the impervious surfaces derived from the fused datasets. View Full-Text
Keywords: decision-level fusion; impervious surface; random forest and Dempster-Shafer theory; GF-1 and Sentinel-1A data decision-level fusion; impervious surface; random forest and Dempster-Shafer theory; GF-1 and Sentinel-1A data
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Shao, Z.; Fu, H.; Fu, P.; Yin, L. Mapping Urban Impervious Surface by Fusing Optical and SAR Data at the Decision Level. Remote Sens. 2016, 8, 945.

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