Wetland Classification Through Integration of GF-3 SAR and Sentinel-2B Multispectral Data over the Yellow River Delta

LI Peng; LI Dahui; LI Zhenhong; WANG Houjie

doi:10.13203/j.whugis20180258

Volume 44 Issue 11

Nov. 2019

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Geomatics and Information Science of Wuhan University > 2019 > 44(11): 1641-1649. > DOI: 10.13203/j.whugis20180258

LI Peng, LI Dahui, LI Zhenhong, WANG Houjie. Wetland Classification Through Integration of GF-3 SAR and Sentinel-2B Multispectral Data over the Yellow River Delta[J]. Geomatics and Information Science of Wuhan University, 2019, 44(11): 1641-1649. DOI: 10.13203/j.whugis20180258

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Wetland Classification Through Integration of GF-3 SAR and Sentinel-2B Multispectral Data over the Yellow River Delta

LI Peng^{1, 2,},
LI Dahui^{1, 3},
LI Zhenhong^{1, 4, ,},
WANG Houjie^{1, 2}

1.
Key Laboratory of Submarine Geosciences and Prospecting Techniques, MOE, Ocean University of China, Qingdao 266100, China
2.
Laboratory of Marine Geology, Pilot National Laboratory for Marine Science and Technology(Qingdao), Qingdao 266061, China
3.
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
4.
School of Engineering, Newcastle University, Newcastle Upon Tyne NE1 7RU, UK

Funds:

The National Natural Science Foundation of China 41806108

National Key Research and Development Program of China 2017YFE0133500

National Key Research and Development Program of China 2016YFA0600903

Shandong Provincial Natural Science Foundation ZR2016DB30

China Postdoctoral Science Foundation 2016M592248

Qingdao Indigenous Innovation Program 16-5-1-25-jch

Fundamental Research Funds for the Central Universities 201713039

Qingdao Postdoctoral Application Research Project

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Author Bio:
LI Peng, PhD, lecturer, specializes in remote sensing of coastal environment. E-mail:pengli@ouc.edu.cn
Corresponding author:
LI Zhenhong, professor, PhD. E-mail:zhenhong.li@newcastle.ac.uk
Received Date: November 03, 2018
Published Date: November 04, 2019

Graphical Abstract

Abstract

Abstract

It is of great significance to monitor dynamic change of wetland over the Yellow River Delta for rational utilization, development and protection of wetland resources. Both Gaofen-3 (GF-3) SAR data and Sentinel-2B multispectral data were used to analyze the spectral, index, polarization scatter and texture feature information of seven types of ground objects over the Yellow River Delta wetland, and then supervised classification was implemented with maximum likelihood (ML), decision tree (DT) and support vector machine (SVM) classifier. The performances of both the joint and the individual classifications with GF-3 and Sentinel-2B data were also evaluated. The results of three algorithms show that the overall accuracy of the joint classification can reach 90.4%, 95.4%, 95.7%, significantly higher than that of the individual classifications, showing the promising potential of GF-3 SAR and Sentinel-2B multi-spectral images in joint wetland classification.
- the Yellow River Delta,
- GF-3,
- Sentinel-2B,
- wetland classification,
- supervised classification

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References

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Wetland Classification Through Integration of GF-3 SAR and Sentinel-2B Multispectral Data over the Yellow River Delta

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