Coastline detection using optical and synthetic aperture radar images. Issue 1 (1st July 2022)

• Record Type: Journal Article
• Authors: Yu, T.; Xu, S.W.; Tao, B.Y.; Shao, W.Z.
• Is Part Of: Advances in space research. Volume 70:Issue 1(2022)
• Page Start: 70
• Page End: 84
• Publication Date: 2022-07-01
• Subjects: Waterline extraction -- Coastline detection -- Optical remote sensing -- Gaofen-3 -- Synthetic-aperture radar; Space sciences -- Periodicals; Astronautics -- Periodicals; Geophysics -- Periodicals; 500.505
• DOI: 10.1016/j.asr.2022.04.030
• Languages: English
• ISSNs: 0273-1177

Abstract

Abstract: Constant temporal and spatial monitoring of the coastline is essential for environmental protection. Waterlines were extracted from multi-source satellite remote sensing images, e.g., Landsat8, MODIS, HY-1C, and GF-3 SAR. A semi-automatic methodology of threshold segmentation was proposed to detect coastline based on local blocks of images, which is suitable for both optical and microwave remote sensing images and threshold scaling standards across different coastline types. The accuracy assessment of artificial, sandy, and muddy coastlines for each dataset was highly dependent on spatial resolution, and the result was GF-3 SAR. < Landsat8 < HY-1C < MODIS for RMSE and STD, and Landsat8 > HY-1C > MODIS > GF-3 SAR for accuracy within 1 pixel. For the angle of imaging effects of them, GF-3 SAR was inferior to other datasets on muddy coastline. The extraction effect of HY-1C was more closely correlated to the tide and its imaging effect of muddy coastline was the best among all datasets we selected. This work fused HY-1C images, which might have omitted some coastline information due to cloud interference, with GF-3 SAR images recorded over the same time period. The result showed that the fusion of optical and microwave remote sensing is effective and allows for better monitoring of the coastline.