Research on adaptive trend surface filtering algorithm for multi-beam sounding data based on K-D tree

Abstract

In the area with complex terrain changes, the traditional trend surface filtering has the problem that it is impossible to reasonably construct the terrain of the expression area. This paper proposes an adaptive trend surface filtering method based on K-D (K-Dimensional Tree) tree. Based on the K-D tree index, the algorithm divides the MBES (the multi-beam echo sounding system) data into several sub-blocks, and then analyzes each sub-block using trend surface filtering algorithm to more accurately reflect the real terrain. The experimental results show that the algorithm execution time in this case is about twice that of the traditional trend surface filtering in the case of millions of data volumes, and the execution efficiency is within a reasonable range. Compared with the traditional trend surface filtering algorithm, the algorithm has a higher fitting degree with the seabed terrain, and the depth difference distribution between the topographic point and the fitting plane is more concentrated. In addition, the proposed algorithm can effectively identify the outlier noise and the near-field noise in the case of ensuring the authenticity of the terrain, so it provides a useful reference for the denoising processing of MBES.