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Segmentation of Multibeam Echosounder Bathymetry and Backscatter

Jeremy Paul Coffelt; Amos Smith; Niklas Conen; Peter Kampmann
In: OCEANS 2024 Singapore. OCEANS MTS/IEEE Conference (OCEANS-2024), April 14-18, Singapore, Singapore, IEEE, 2024.


Multibeam echosounders (MBES) are the tool of choice for high-precision underwater surveys, especially when water conditions render optical imagery ineffective. We present and evaluate the following approaches for MBES segmentation: (1) real-time processing of single sounding profiles using traditional machine learning techniques, (2) batch processing of “waterfall” pseudo-images using a standard U-Net model, (3) the same model adapted to 2D projections of 3D point clouds, and (4) post-mission, survey-level processing using modern networks specifically designed for sparse point clouds. Strengths and weaknesses of the methods are discussed, including data preprocessing requirements, robustness, and ease of implementation/interpretation. Evaluation is performed on real data collected by an autonomous underwater vehicle (AUV) during a deep-sea industrial pipeline inspection.