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.
Highlights
In recent years, research on seabed information is a focus point in marine research [1]
Aiming at the above problems, this paper proposes a spatial adaptive trend surface filtering algorithm based on K-D tree index
The above two filtering algorithms are used to process the same set of point cloud data, and the traditional trend surface filtering is named as algorithm one, and the adaptive trend surface filtering algorithm based on K-D tree index is named algorithm 2
Summary
Research on seabed information is a focus point in marine research [1]. Literature [11] proposed a trend surface filtering method, which uses a polynomial surface function to fit the seabed topography This algorithm is simple to calculate and highly sensitive to abnormal noises far from the terrain. The algorithm is not sensitive to the near-ground noise, the trend surface fitted by the algorithm is susceptible to noise and is not robust In response to this problem, the literature [12] proposed the detection and elimination of abnormal sounding values based on the truncated least squares estimation. The literature [13] proposed an iterative trend surface fitting algorithm based on robust estimation This algorithm has a high sensitivity to noise, but it needs to determine the weight of the massive point cloud and iterate it, so it is difficult to ensure the operation efficiency. In order to verify the effectiveness of the algorithm, design experiment compares the execution efficiency and denoising effect of the algorithm and traditional trend surface filtering algorithm
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