A Water Body Extraction Methods Comparison Based on FengYun Satellite Data: A Case Study of Poyang Lake Region, China

Xufeng Wei; Wenbo Xu; Kuanle Bao; Weimin Hou; Jia Su; Haining Li; Zhuang Miao

doi:10.3390/rs12233875

Remote Sensing (Nov 2020)

A Water Body Extraction Methods Comparison Based on FengYun Satellite Data: A Case Study of Poyang Lake Region, China

Xufeng Wei,
Wenbo Xu,
Kuanle Bao,
Weimin Hou,
Jia Su,
Haining Li,
Zhuang Miao

Affiliations

Xufeng Wei: School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 610054, China
Wenbo Xu: School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 610054, China
Kuanle Bao: School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 610054, China
Weimin Hou: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
Jia Su: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
Haining Li: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
Zhuang Miao: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China

DOI: https://doi.org/10.3390/rs12233875
Journal volume & issue: Vol. 12, no. 23
p. 3875

Abstract

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Water body extraction can help eco-environmental policymakers to intuitively grasp surface water resources. Remote sensing technology can accurately and quickly extract surface water information, which is of great significance for monitoring surface water changes. Fengyun satellite images have the advantages of high time resolution and multispectral bands. This provides important image data suitable for high-frequency surface water monitoring. Based on Fengyun 3 medium resolution spectral imager (FY-3/MERSI) data, 7 methods were applied in this study, which include single-band threshold method, water body index method, knowledge decision tree classification method, supervised classification method, unsupervised classification method, spectral matching based on discrete particle swarm optimization (SMDPSO), and improved spectral matching based on discrete particle swarm optimization with linear feature enhancement (SMDPSO+LFE). These methods were used to extract the land surface water of Poyang Lake, check the samples from the Landsat image with similar times to the FY-3 images, and calculate the classification accuracy via the confusion matrix. The results showed that the overall classification accuracy (OA) of the SMDPSO+LFE is 97.64%, and the Kappa coefficient is 0.95. To analyze the stability of the surface water extracted by SMDPSO+LFE in different regions, this paper selected eight test sites with different surface water types, landscapes, and terrains to extract surface water. Based on an analysis of the land surface water results at the eight test sites, every OA in the eight sites was higher than 94.5%, the Kappa coefficient was greater than 0.88. In conclusion, the SMDPSO+LFE is found to be the most suitable method among the 7 methods and effectively distinguish between different surface water bodies and backgrounds with good stability.

Published in Remote Sensing

ISSN: 2072-4292 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science
Website: http://www.mdpi.com/journal/remotesensing/

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