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Published version

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2022-05-01

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cc-by (c) Martí, Aniol et al., 2022
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/195371

Compression of Multibeam Echosounders Bathymetry and Water Column Data

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Related resource

https://doi.org/10.3390/rs14092063

Abstract

Over the past decade, Multibeam Echosounders (MBES) have become one of the most used techniques in sea exploration. Modern MBES are capable of acquiring both bathymetric information on the seafloor and the reflectivity of the seafloor and water column. Water column imaging MBES surveys acquire significant amounts of data with rates that can exceed several GB/h depending on the ping rate. These large file sizes obtained from recording the full water column backscatter make remote transmission difficult if not prohibitive with current technology and bandwidth limitations. In this paper, we propose an algorithm to decorrelate water column and bathymetry data, focusing on the KMALL format released by Kongsberg Maritime in 2019. The pre-processing stage is integrated into FAPEC, a data compressor originally designed for space missions. Here, we test the algorithm with three different datasets: two of them provided by Kongsberg Maritime and one dataset from the Gulf of Mexico provided by Fugro USA Marine. We show that FAPEC achieves good compression ratios at high speeds using the pre-processing stage proposed in this paper. We also show the advantages of FAPEC over other lossless compressors as well as the quality of the reconstructed water column image after lossy compression at different levels. Lastly, we test the performance of the pre-processing stage, without the constraint of an entropy encoder, by means of the histograms of the original samples and the prediction errors

Subject

Fons marins, Geologia submarina

Subject (English)

Ocean bottom, Submarine geology

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Articles publicats en revistes (Dinàmica de la Terra i l'Oceà)

Citation

MARTÍ, Aniol, et al. Compression of Multibeam Echosounders Bathymetry and Water Column Data. Remote Sensing. 2022. Vol. 14, num. 9, pags. 2063. ISSN 2072-4292. [consulted: 14 of August of 2026]. Available at: https://hdl.handle.net/2445/195371

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