Shallow water bathymetry based on a back propagation neural network and ensemble learning using multispectral satellite imagery

The back propagation (BP) neural network method is widely used in bathymetry based on multispectral satellite imagery. However, the classical BP neural network method faces a potential problem because it easily falls into a local minimum, leading to model training failure. This study confirmed that the local minimum problem of the BP neural network method exists in the bathymetry field and cannot be ignored. Furthermore, to solve the local minimum problem of the BP neural network method, a bathymetry method based on a BP neural network and ensemble learning (BPEL) is proposed. First, the remote sensing imagery and training sample were used as input datasets, and the BP method was used as the base learner to produce multiple water depth inversion results. Then, a new ensemble strategy, namely the minimum outlying degree method, was proposed and used to integrate the water depth inversion results. Finally, an ensemble bathymetric map was acquired. Anda Reef, northeastern Jiuzhang Atoll, and Pingtan coastal zone were selected as test cases to validate the proposed method. Compared with the BP neural network method, the root-mean-square error and the average relative error of the BPEL method can reduce by 0.65-2.84 m and 16%-46% in the three test cases at most. The results showed that the proposed BPEL method could solve the local minimum problem of the BP neural network method and obtain highly robust and accurate bathymetric maps.