Abstract

The present study adopts a multi-sensor approach that utilizes optical remote sensing and Synthetic Aperture Radar (SAR) data complimentarily for coastal bathymetry estimation. The depths between 0–15 m are derived from Landsat 8 OLI imageries, by using the machine learning approach of Support Vector Regression (SVR); and high accuracies with RMSEs ranging between 0.17 m−0.95 m are obtained for the chosen study regions. Further, ALOS PALSAR images are used to obtain depths beyond 15 m, by applying the Wave Based Approach (WBA) which is based on the wave transformation and linear dispersion principles. The estimated depths are compared with the bathymetry obtained from admiralty charts or GEBCO and RMSEs between 0.83–1.34m are observed. Finally, the outputs of the SVR and WBA are combined to derive depths between 0–50 m at 75 m spatial resolution. The resultant depth maps agree with those obtained from GEBCO, with an additional advantage of higher accuracy in the near-shore region.

摘要

本研究采用多传感器方法，利用光学遥感和合成孔径雷达 (SAR) 数据互补地进行海岸测深估计。0-15 m 之间的深度来自 Landsat 8 OLI 图像，使用支持向量回归 (SVR) 的机器学习方法；对于选定的研究区域，可以获得 RMSE 范围在 0.17 m-0.95 m 之间的高精度。此外，通过应用基于波变换和线性色散原理的基于波的方法 (WBA)，ALOS PALSAR 图像用于获得超过 15 m 的深度。将估计的深度与从金钟图或 GEBCO 获得的水深进行比较，并观察到 ​​0.83-1.34m 之间的 RMSE。最后，结合 SVR 和 WBA 的输出，以 75 m 的空间分辨率得出 0-50 m 之间的深度。