Duan Zhixin, Chu Sensen, Cheng Liang, Ji Chen, Li Manchun, Shen Wei
Opt Express. 2022 Jan 31;30(3):3238-3261. doi: 10.1364/OE.444557.
Satellite-derived bathymetry (SDB) has an extensive prospect in nearshore bathymetry for its high efficiency and low costs. Atmospheric correction and bathymetric modeling are critical processes in SDB, and examining the performance of related algorithms and models will contribute to the formulation of reliable bathymetry strategies. This study explored the effectiveness of three general atmospheric correction algorithms, namely Second Simulation of a Satellite Signal in the Solar Spectrum (6S), Atmospheric correction for OLI 'lite' (ACOLITE), and QUick Atmospheric Correction (QUAC), in depth retrieval from Landsat-8 and Sentinel-2A images using different SDB models over Ganquan Island and Oahu Island. The bathymetric Light Detection and Ranging (LiDAR) data was used for SDB model training and accuracy verification. The results indicated that the three atmospheric correction algorithms could provide effective corrections for SDB. For the SDB models except log-transformed band ratio model (LBR) and support vector machine (SVM), the impact of different atmospheric corrections on bathymetry was basically the same. Furthermore, we assessed the performance of six different SDB models: Lyzenga's model (LM), generalized additive model (GAM), LBR, SVM, multilayer perceptron (MLP), and random forest (RF). The bathymetric accuracy, consistency of bathymetric maps and generalization ability were considered for the assessment. Given sufficient training data, the accuracy of the machine learning models (SVM, MLP, RF) was generally superior to that of the empirical inversion models (LM, GAM, LBR), with the root mean square error (RMSE) varied between 0.735 m to 1.177 m. MLP achieved the best accuracy and consistency. When the depth was deeper than 15 m, the bathymetry error of all the SDB models increased sharply, and LM, LBR and SVM reached the upper limit of depth retrieval capability at 20-25 m. In addition, LM and LBR were demonstrated to have better adaptability in heterogeneous environment without training data.
星载测深（SDB）因其高效性和低成本，在近岸测深领域具有广阔前景。大气校正和测深建模是SDB中的关键过程，研究相关算法和模型的性能将有助于制定可靠的测深策略。本研究利用甘泉岛和瓦胡岛不同的SDB模型，深入探讨了三种常用大气校正算法，即太阳光谱卫星信号二次模拟（6S）、OLI“lite”大气校正（ACOLITE）和快速大气校正（QUAC），在从Landsat - 8和Sentinel - 2A影像进行深度反演方面的有效性。测深激光雷达（LiDAR）数据用于SDB模型训练和精度验证。结果表明，这三种大气校正算法可为SDB提供有效的校正。对于除对数变换波段比值模型（LBR）和支持向量机（SVM）之外的SDB模型，不同大气校正对测深的影响基本相同。此外，我们评估了六种不同的SDB模型：利曾加模型（LM）、广义相加模型（GAM）、LBR、SVM、多层感知器（MLP）和随机森林（RF）。评估考虑了测深精度、测深图的一致性和泛化能力。在有足够训练数据的情况下，机器学习模型（SVM、MLP、RF）的精度通常优于经验反演模型（LM、GAM、LBR），均方根误差（RMSE）在0.735米至1.177米之间变化。MLP实现了最佳的精度和一致性。当深度超过15米时，所有SDB模型的测深误差急剧增加，LM、LBR和SVM在20 - 25米处达到深度反演能力的上限。此外，在没有训练数据的异质环境中，LM和LBR表现出更好的适应性。
