Detecting Outlier of Multibeam Sounding with BP Neural Network

A method of detecting outlier of multibeam sounding with back propagation (BP) neural network is proposed in this paper for the complexity of bathymetric data of a ping. This paper constructs a training and learning algorithm for complex curve of multibeam single ping data for curve fitting based on the mapping function from input to output of BP neural network. Then it inspects the results from the previous steps lengthways by the correlation analysis of data of adjacent pings, and a vertical check to locate and remore outlier is also proposed. The experiment is conducted using the real bathymetric data, where there is a shipwreck in the middle. And also the result is compared with the combined uncertainty and bathymetry estimator (CUBE) algorithm, which is a popular method in detecting outlier of multibeam sounding at present. The experiment proves that the method proposed in this paper can detect the outlier more effectively.