Seabed sediment classification of side scan sonar data using histogram approach

Side scan sonar (SSS) delivered an advantage for sediment classification studies. In particular, the availability of backscatter intensity offers an alternative method to study seafloor hardness and softness. Numerous seabed mapping processes used many kinds of classification techniques can produce...

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Main Author: Mohd. Azlan Jamal, Nur Aina
Format: Thesis
Language:English
Published: 2022
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Online Access:http://eprints.utm.my/id/eprint/100407/1/NurAinaAzlanMBE2022.pdf
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spelling my.utm.1004072023-04-13T03:18:02Z http://eprints.utm.my/id/eprint/100407/ Seabed sediment classification of side scan sonar data using histogram approach Mohd. Azlan Jamal, Nur Aina G70.212-70.215 Geographic information system Side scan sonar (SSS) delivered an advantage for sediment classification studies. In particular, the availability of backscatter intensity offers an alternative method to study seafloor hardness and softness. Numerous seabed mapping processes used many kinds of classification techniques can produce sediment maps from backscatter images, ranging from simple clustering to machine learning approaches. This study aims to perform a pixel grouping method for backscatter images from SSS using the histogram generated from the backscatter intensities. The aim will be achieved through three (3) objectives; to classify the seabed characteristic using histogram classification, to produce a sediment map through a histogram classification created and, lastly to test the model's validity using ground-truthing data ground-truthing data such as sediment distribution and coral video transect. Acoustic data from the SSS data acquired in Labuan Marine Park was used in this study. The 900 kHz side scan data was processed and corrected using SonarWiz 7 software. The data were then categorised the pixel intensities based on the histogram shape. A few data classify techniques were tested to produce classification maps using equal intervals, quantile methods, natural breaks, and, geometrical intervals. Classification maps derived from these methods were then validated with ground truth samples collected using underwater videos and sediment grabs to assess their accuracies via qualitative assessment. The result shows that geometrical interval was the only method that relatively complemented the ground truth data and works reasonably well. Therefore, this can be a good tool in designing management programs for the marine park to know the general view of the sediment distribution in that area. It creates a simple and straightforward but statistically robust objective of general overview based on geophysical data provided. 2022 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/100407/1/NurAinaAzlanMBE2022.pdf Mohd. Azlan Jamal, Nur Aina (2022) Seabed sediment classification of side scan sonar data using histogram approach. Masters thesis, Universiti Teknologi Malaysia. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:150203
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 G70.212-70.215 Geographic information system
spellingShingle G70.212-70.215 Geographic information system
Mohd. Azlan Jamal, Nur Aina
Seabed sediment classification of side scan sonar data using histogram approach
description Side scan sonar (SSS) delivered an advantage for sediment classification studies. In particular, the availability of backscatter intensity offers an alternative method to study seafloor hardness and softness. Numerous seabed mapping processes used many kinds of classification techniques can produce sediment maps from backscatter images, ranging from simple clustering to machine learning approaches. This study aims to perform a pixel grouping method for backscatter images from SSS using the histogram generated from the backscatter intensities. The aim will be achieved through three (3) objectives; to classify the seabed characteristic using histogram classification, to produce a sediment map through a histogram classification created and, lastly to test the model's validity using ground-truthing data ground-truthing data such as sediment distribution and coral video transect. Acoustic data from the SSS data acquired in Labuan Marine Park was used in this study. The 900 kHz side scan data was processed and corrected using SonarWiz 7 software. The data were then categorised the pixel intensities based on the histogram shape. A few data classify techniques were tested to produce classification maps using equal intervals, quantile methods, natural breaks, and, geometrical intervals. Classification maps derived from these methods were then validated with ground truth samples collected using underwater videos and sediment grabs to assess their accuracies via qualitative assessment. The result shows that geometrical interval was the only method that relatively complemented the ground truth data and works reasonably well. Therefore, this can be a good tool in designing management programs for the marine park to know the general view of the sediment distribution in that area. It creates a simple and straightforward but statistically robust objective of general overview based on geophysical data provided.
format Thesis
author Mohd. Azlan Jamal, Nur Aina
author_facet Mohd. Azlan Jamal, Nur Aina
author_sort Mohd. Azlan Jamal, Nur Aina
title Seabed sediment classification of side scan sonar data using histogram approach
title_short Seabed sediment classification of side scan sonar data using histogram approach
title_full Seabed sediment classification of side scan sonar data using histogram approach
title_fullStr Seabed sediment classification of side scan sonar data using histogram approach
title_full_unstemmed Seabed sediment classification of side scan sonar data using histogram approach
title_sort seabed sediment classification of side scan sonar data using histogram approach
publishDate 2022
url http://eprints.utm.my/id/eprint/100407/1/NurAinaAzlanMBE2022.pdf
http://eprints.utm.my/id/eprint/100407/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:150203
_version_ 1764222562494054400
score 13.18916