A multibeam outlier detection algorithm based on density clustering

Jiayu Zhang; Tao Zhang

doi:10.1117/12.3014694

8 December 2023 A multibeam outlier detection algorithm based on density clustering

Jiayu Zhang, Tao Zhang

Proceedings Volume 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023); 129430Z (2023) https://doi.org/10.1117/12.3014694
Event: International Workshop on Signal Processing and Machine Learning (WSPML 2023), 2023, Hangzhou, ZJ, China

Abstract

The multibeam echo sounder (MBES) is widely used in oceanic exploration for various purposes such as submarine topographic surveying, geological investigations, and salvage operations for submarine wrecks. The multibeam data is prone to measurement errors due to various factors such as the intricate acoustic environment in the ocean, motion sensor errors, and sonar echo multipath interference. The task of automatically cleaning MBES datasets remains a challenging one. This study proposes an automatic cleansing strategy for MBES datasets based on the density clustering method to identify and reject anomalous data by continuous clustering of actual topographic data. The analysis initially focuses on the characteristics of various types of multibeam data outliers. Then, the study proposes an improved OPTICS method for detecting outliers in multibeam data. The RD computed through OPTICS serves to detect isolated outliers, while topological analysis is employed to identify structural outliers. Finally, the proposed algorithm is tested on MBES data collected by iBeam 8140 sonar sensor.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiayu Zhang and Tao Zhang "A multibeam outlier detection algorithm based on density clustering", Proc. SPIE 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023), 129430Z (8 December 2023); https://doi.org/10.1117/12.3014694

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