Abstract
Abstract Remote sensing (RS) water depth inversion is an important technology and the method of water depth measurement. Taking the waters around the islands outside the Pearl River Estuary as an example, five optical RS depth inversion algorithms were introduced. Then, five water depth inversion models were trained through the HJ-1B satellite RS image and the measured water depth data. The results show that the mean absolute error (MAE) of the deep learning model was the smallest (2.350 m), and that the distribution of predicted water depth points was closest to the actual value. Deep learning has been widely used in RS image classification and recognition and shows its advantages. Therefore, the deep learning model was applied to extract the depth of the shallow water. Meanwhile, the obtained inversion effect map is closest to the actual contour map. The water depth inversion performance of back propagation neural network model is better than that of the radial basis function (RBF) neural network model. Besides, the inversion accuracy of the RBF neural network may be affected due to the small amount of data and the improper number of hidden neurons. The results show broad application prospects of machine learning algorithms in RS water depth inversion. Also, this study provided data support for model optimization, training, and parameter setting.
Highlights
The ocean plays a vital role in the strategic layout of national security and economical construction, and the water depth is the essential marine element in shallow seas
Solar radiation is weakened by the atmosphere before reaching the surface of the water body, and only few part of the energy is reflected to the atmosphere at the water–air interface
The theoretical interpretation model required a small number of parameters and only required that the input optical radiation data of the local water body can be used to derive the global water depth inversion results, and the inversion accuracy was better
Summary
The ocean plays a vital role in the strategic layout of national security and economical construction, and the water depth is the essential marine element in shallow seas. The semi-theoretical and semi-empirical model combined theoretical interpretation models and empirical algorithms, and performed regression analysis through measured parameter data to obtain the correlation between reflectivity and water depth, and inverted the water depth of the entire area. The theoretical interpretation model required a small number of parameters and only required that the input optical radiation data of the local water body can be used to derive the global water depth inversion results, and the inversion accuracy was better. The data objects are as follows: sunlight radiating on the water surface, water body-atmospheric scattering, and water body bottom reflection, to establish the relationship between the measured area water depth value and the RS optical image spectrum. Sandidge and Holyer introduced machine learning to water depth inversion, combining the neural network method with water depth RS inversion for the first time. The characteristics of the study area and the characteristics of the HJ-B satellite data were explained
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
