Automatic filtering of far outliers in multibeam echo sounding dataset using robust detection algorithms

Bathymetric data collections using multibeam echo sounder (MBES) have led to increasing data rates and densities. While it is really advantage having full coverage of seabed, data management is the utmost aspect to establish. In this data collection method, part of the dataset contains erroneous dat...

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Main Authors: Mahmud, Mohd. Razali, Mohd. Yusof, Othman
Format: Conference or Workshop Item
Language:English
Published: 2005
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Online Access:http://eprints.utm.my/id/eprint/1376/1/Paper094Razali.pdf
http://eprints.utm.my/id/eprint/1376/
http://www.civil.eng.usm.my/isg2005/home.shtml
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spelling my.utm.13762017-08-28T04:08:37Z http://eprints.utm.my/id/eprint/1376/ Automatic filtering of far outliers in multibeam echo sounding dataset using robust detection algorithms Mahmud, Mohd. Razali Mohd. Yusof, Othman TA Engineering (General). Civil engineering (General) Bathymetric data collections using multibeam echo sounder (MBES) have led to increasing data rates and densities. While it is really advantage having full coverage of seabed, data management is the utmost aspect to establish. In this data collection method, part of the dataset contains erroneous data, as measurements are always associated with uncertainties. The critical task for hydrographic surveyor is to make decision on which data can be accepted as good data and the remaining data will be considered as outliers. As there is no ground truth available for the MBES data to compare with, the best solution to address this problem is by using statistical outliers elimination. In order to obtain meaningful results when statistical tools are in used, the dataset should be in a Gaussian distribution. To ensure that the dataset in a bell-shaped curve characteristic, the far outliers must be eliminated prior to any processing. This certainly needs further considerations on characteristics of the erroneous data. Thus, a post-processing program was developed to detect and discard the MBES far outliers based on behaviours of propagated beam in the multibeam sonar system. The entire data have to go through a series of far outliers screening. A remarkable result can be achieved by filtering these far outliers using automatic detection mode. This paper elaborates the techniques used for the detection and elimination of the far outliers in the MBES dataset, known as robust detection algorithms. It also explains on the filtering sequences used and results produced by the developed program. 2005-09 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/1376/1/Paper094Razali.pdf Mahmud, Mohd. Razali and Mohd. Yusof, Othman (2005) Automatic filtering of far outliers in multibeam echo sounding dataset using robust detection algorithms. In: International Symposium & Exhibition on Geoinformation 2005 Geospatial Solutions for Managing the Borderless World,, 27 - 29 September 2005, Pulau Pinang. http://www.civil.eng.usm.my/isg2005/home.shtml
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Mahmud, Mohd. Razali
Mohd. Yusof, Othman
Automatic filtering of far outliers in multibeam echo sounding dataset using robust detection algorithms
description Bathymetric data collections using multibeam echo sounder (MBES) have led to increasing data rates and densities. While it is really advantage having full coverage of seabed, data management is the utmost aspect to establish. In this data collection method, part of the dataset contains erroneous data, as measurements are always associated with uncertainties. The critical task for hydrographic surveyor is to make decision on which data can be accepted as good data and the remaining data will be considered as outliers. As there is no ground truth available for the MBES data to compare with, the best solution to address this problem is by using statistical outliers elimination. In order to obtain meaningful results when statistical tools are in used, the dataset should be in a Gaussian distribution. To ensure that the dataset in a bell-shaped curve characteristic, the far outliers must be eliminated prior to any processing. This certainly needs further considerations on characteristics of the erroneous data. Thus, a post-processing program was developed to detect and discard the MBES far outliers based on behaviours of propagated beam in the multibeam sonar system. The entire data have to go through a series of far outliers screening. A remarkable result can be achieved by filtering these far outliers using automatic detection mode. This paper elaborates the techniques used for the detection and elimination of the far outliers in the MBES dataset, known as robust detection algorithms. It also explains on the filtering sequences used and results produced by the developed program.
format Conference or Workshop Item
author Mahmud, Mohd. Razali
Mohd. Yusof, Othman
author_facet Mahmud, Mohd. Razali
Mohd. Yusof, Othman
author_sort Mahmud, Mohd. Razali
title Automatic filtering of far outliers in multibeam echo sounding dataset using robust detection algorithms
title_short Automatic filtering of far outliers in multibeam echo sounding dataset using robust detection algorithms
title_full Automatic filtering of far outliers in multibeam echo sounding dataset using robust detection algorithms
title_fullStr Automatic filtering of far outliers in multibeam echo sounding dataset using robust detection algorithms
title_full_unstemmed Automatic filtering of far outliers in multibeam echo sounding dataset using robust detection algorithms
title_sort automatic filtering of far outliers in multibeam echo sounding dataset using robust detection algorithms
publishDate 2005
url http://eprints.utm.my/id/eprint/1376/1/Paper094Razali.pdf
http://eprints.utm.my/id/eprint/1376/
http://www.civil.eng.usm.my/isg2005/home.shtml
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score 13.160551