School of Geosciences and Info-physics, Central South University, Changsha 410083, China.
College of Resources and Environmental Science, Hunan Normal University, Changsha 410081, China.
Sensors (Basel). 2018 Jun 11;18(6):1898. doi: 10.3390/s18061898.
The coastline detection is one of the main applications of the Gaofen-3 satellite in the ocean field. However, the capability of Gaofen-3 SAR image in coastline detection has not yet been validated. In this paper, two Gaofen-3 SAR images, acquired in 2016, were used to extract the coastlines of the regions of Bohai and Taihu in China, respectively. The classical Fuzzy C-means (FCM) method was used in the coastline detection, but had been improved by combining the Wavelet decomposition algorithm to better suppress the inherent speckle noises of SAR image. Coastline detection results obtained from two Sentinel-1 SAR images acquired on the same regions were compared with those of the Gaofen-3 images. By using the manually delineated coastlines as the standards in the qualitative evaluations, improvements of about 12.0%, 8.3%, 23.8%, and 9.4% can be achieved by the improved FCM method with respect to the indicators of mean, RMSE, PGSD, and P90%, respectively; demonstrating that the Gaofen-3 data is superior to the Sentinel-1 data in the detection of coastline.
海岸线检测是高分三号卫星在海洋领域的主要应用之一。然而，高分三号 SAR 图像在海岸线检测方面的性能尚未得到验证。本文使用 2016 年获取的两幅高分三号 SAR 图像，分别提取了中国渤海和太湖地区的海岸线。经典的模糊 C 均值（FCM）方法用于海岸线检测，但通过结合小波分解算法进行了改进，以更好地抑制 SAR 图像固有的斑点噪声。将在同一地区获取的两幅 Sentinel-1 SAR 图像的海岸线检测结果与高分三号图像的结果进行了比较。通过将手动勾画的海岸线作为定性评价的标准，改进后的 FCM 方法在均值、均方根误差、PGSD 和 P90%等指标上分别提高了约 12.0%、8.3%、23.8%和 9.4%；表明在海岸线检测方面，高分三号数据优于 Sentinel-1 数据。
