Appl Opt. 2023 Mar 10;62(8):2017-2029. doi: 10.1364/AO.480698.
As a significant and cost-effective method of obtaining shallow seabed topography, satellite derived bathymetry (SDB) can acquire a wide range of shallow sea depth by integrating a small quantity of in-situ water depth data. This method is a beneficial addition to traditional bathymetric topography. The seafloor's spatial heterogeneity leads to inaccuracies in bathymetric inversion, which reduces bathymetric accuracy. By utilizing multispectral data with multidimensional features, an SDB approach incorporating spectral and spatial information of multispectral images is proposed in this study. In order to effectively increase the accuracy of bathymetry inversion throughout the entire area, first the random forest with spatial coordinates is established to control bathymetry spatial variation on a large scale. Next, the Kriging algorithm is used to interpolate bathymetry residuals, and the interpolation results are used to adjust bathymetry spatial variation on a small scale. The data from three shallow water sites are experimentally processed to validate the method. Compared with other established bathymetric inversion techniques, the experimental results show that the method effectively reduces the error in bathymetry estimation caused by spatial heterogeneity of the seabed, producing high-precision inversion bathymetry with a root mean square error of 0.78 to 1.36 meters.
卫星测深（SDB）作为获取浅海海底地形的一种重要且经济有效的方法，通过整合少量的现场水深数据，可以获取广泛的浅海深度范围。该方法是传统测深地形学的有益补充。海底的空间异质性导致测深反演的不准确，从而降低了测深的准确性。本研究提出了一种利用多光谱图像的光谱和空间信息的 SDB 方法，该方法利用具有多维特征的多光谱数据。为了有效地提高整个区域的测深反演精度，首先建立带有空间坐标的随机森林，以控制大规模的测深空间变化。然后，使用克里金算法对测深残差进行插值，将插值结果用于调整小尺度的测深空间变化。对三个浅水区域的数据进行实验处理，以验证该方法。与其他已建立的测深反演技术相比，实验结果表明，该方法有效地减少了海底空间异质性引起的测深估计误差，生成了具有均方根误差为 0.78 至 1.36 米的高精度反演测深图。
