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Coastline Detection with Gaofen-3 SAR Images Using an Improved FCM Method
Meng An1, Qian Sun2,3, Jun Hu4,5
1School of Geosciences and Info-physics, Central South University, Changsha 410083, China. AnMeng@csu.edu.cn.
Sensors (Basel, Switzerland)
|June 13, 2018
Summary
This study validates the Gaofen-3 satellite
Area of Science:
- Remote Sensing
- Oceanography
- Geospatial Analysis
Background:
- Coastline detection is crucial for oceanographic applications.
- The capability of Gaofen-3 Synthetic Aperture Radar (SAR) images for coastline detection required validation.
- Existing methods faced challenges with speckle noise in SAR imagery.
Purpose of the Study:
- To evaluate the effectiveness of Gaofen-3 SAR images in coastline detection.
- To compare the performance of Gaofen-3 data against Sentinel-1 data for this application.
- To introduce an improved coastline detection method for SAR images.
Main Methods:
- Utilized two Gaofen-3 SAR images from 2016 for coastline extraction in Bohai and Taihu regions.
- Applied an improved Fuzzy C-means (FCM) algorithm combined with Wavelet decomposition to suppress speckle noise.
- Compared results with coastline detection from Sentinel-1 SAR images using manually delineated coastlines as ground truth.
Main Results:
- The improved FCM method demonstrated significant enhancements in coastline detection accuracy.
- Quantitative improvements were observed in mean (12.0%), RMSE (8.3%), PGSD (23.8%), and P90% (9.4%) compared to baseline methods.
- Gaofen-3 SAR data proved superior to Sentinel-1 SAR data for coastline detection in the studied regions.
Conclusions:
- The improved FCM method effectively enhances coastline detection from Gaofen-3 SAR images.
- Gaofen-3 satellite imagery offers superior performance for coastline extraction compared to Sentinel-1.
- This validates Gaofen-3's capability and potential for critical oceanographic applications.
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