An Intelligent Detection Method for Different Types of Outliers in Multibeam Bathymetric Point Cloud

Bathymetric Multibeam echo sounder systems (MBESs) are the most effective and reliable way to survey the earth's seafloor. However, multibeam bathymetric data inevitably contain different types of outliers due to measurement characteristics and complex underwater environments. The traditional a...

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Vydané v:IEEE transactions on geoscience and remote sensing Ročník 60; s. 1
Hlavní autori: Yang, Fanlin, Xu, Fangzheng, Fan, Miao, Bu, Xianhai, Tu, Zejie, Yan, Xunpeng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Bathymetric data
Building codes
coding octree
connected component labeling
Data analysis
different types of outliers
Echo sounding
Echoes
Encoding
Geodesy
Geomagnetism
Methods
Multibeam bathymetric point cloud
Ocean floor
Octree coding
Octrees
outlier removal
Outliers (statistics)
Point cloud compression
Reliability
Removal
Sea floor
Surveying
System effectiveness
Three-dimensional displays
ISSN:0196-2892, 1558-0644
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Popis
Shrnutí:Bathymetric Multibeam echo sounder systems (MBESs) are the most effective and reliable way to survey the earth's seafloor. However, multibeam bathymetric data inevitably contain different types of outliers due to measurement characteristics and complex underwater environments. The traditional automatic approach to eliminate outliers may lead to more than one of the questions of reliability, limitation, and efficiency, respectively. This paper offers an algorithm aiming to detect different types of outliers by considering their characteristic of distribution and distance of them, rapidly. First, a coding octree based on Morton code is built to guarantee perfect efficiency and space division. Second, coarse outlier removal is performed by octree-based voxelized representation of the bathymetric data, and outliers far away from the seafloor will be detected and eliminated by connected component labeling. Third, fine outlier removal is employed to delete outliers connected closely to the seafloor by the improved morphological method based on the combination of the k-d tree and octree. Experimental results show that the proposed algorithm can achieve promising results. The percent of outliers that are detected by the hand-edit method that is regarded as a reference result is 5.06%. 4.70% of points in a total number of 3645541 points are detected in our method. Compared with other classical filtering methods, the intelligent method for detecting different types of the outlier from coarse to fine attains favorable performance in a reasonable time, avoiding over-filtering, and demonstrates high reliability for multibeam bathymetric point cloud.
Bibliografia:ObjectType-Article-1
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2022.3209344