Classification of multibeam snippets data using statistical analysis method

The multibeam snippets data, an acoustic backscatter data acquired by the multibeam sonar systems, carries important information about the seafloor and its physical properties, thus aid in seafloor classification. This acoustic backscatter strength is highly dependent of incidence angle due to diffe...

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Main Author:	Lau, Kum Weng
Format:	Thesis
Language:	English
Published:	2012
Subjects:	G Geography (General)
Online Access:	http://eprints.utm.my/id/eprint/34588/5/LauKumWengMFGHT2012.pdf
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id	my-utm-ep.34588
record_format	uketd_dc
spelling	my-utm-ep.345882017-09-21T07:50:15Z Classification of multibeam snippets data using statistical analysis method 2012-11 Lau, Kum Weng G Geography (General) The multibeam snippets data, an acoustic backscatter data acquired by the multibeam sonar systems, carries important information about the seafloor and its physical properties, thus aid in seafloor classification. This acoustic backscatter strength is highly dependent of incidence angle due to different mechanism of scattering with different angular domains. Therefore, it is necessary to perform certain corrections for the backscatter data before producing the hydrographic plan. This is solved with the radiometric correction using CARIS HIPS & SIPS 7.0 software and geometric correction using Matlab programming. Radiometric correction removed the Time Varied Gain from the data while geometric correction corrected the data for local bottom slope, seafloor insonified area and angular dependency. The seafloor can be classified using the produced distribution histogram of the desired study area. It is found that the snippets intensities estimated from the mean of snippets intensities provide an accurate measurement of the actual intensities strength of the seafloor and play an important role in correcting the angular dependency of the data. Besides that, the Gamma distribution model is found to be fitting well with the distribution of snippets intensities. The parameters of the Gamma distribution model, the scale and shape parameters are found to be dependent on the incidence angles of data. Furthermore, the Kolmogorov-Smimoff test was carried out to access the fitting of other statistical distribution models such as the Rayleigh and Log-normal distribution models in fitting with the distribution of snippets intensities. It is shown that the Rayleigh and the Log-normal distribution models followed only with the head of the distribution of the experimental data but not towards the tail of experimental distribution. Further experiment on comparing the backscattering characteristics of snippets data that were collected from different types of seafloor habitats is recommended for future research. 2012-11 Thesis http://eprints.utm.my/id/eprint/34588/ http://eprints.utm.my/id/eprint/34588/5/LauKumWengMFGHT2012.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:70134?site_name=Restricted Repository masters Universiti Teknologi Malaysia, Faculty of Geoinformation and Real Estate Faculty of Geoinformation and Real Estate
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	G Geography (General)
spellingShingle	G Geography (General) Lau, Kum Weng Classification of multibeam snippets data using statistical analysis method
description	The multibeam snippets data, an acoustic backscatter data acquired by the multibeam sonar systems, carries important information about the seafloor and its physical properties, thus aid in seafloor classification. This acoustic backscatter strength is highly dependent of incidence angle due to different mechanism of scattering with different angular domains. Therefore, it is necessary to perform certain corrections for the backscatter data before producing the hydrographic plan. This is solved with the radiometric correction using CARIS HIPS & SIPS 7.0 software and geometric correction using Matlab programming. Radiometric correction removed the Time Varied Gain from the data while geometric correction corrected the data for local bottom slope, seafloor insonified area and angular dependency. The seafloor can be classified using the produced distribution histogram of the desired study area. It is found that the snippets intensities estimated from the mean of snippets intensities provide an accurate measurement of the actual intensities strength of the seafloor and play an important role in correcting the angular dependency of the data. Besides that, the Gamma distribution model is found to be fitting well with the distribution of snippets intensities. The parameters of the Gamma distribution model, the scale and shape parameters are found to be dependent on the incidence angles of data. Furthermore, the Kolmogorov-Smimoff test was carried out to access the fitting of other statistical distribution models such as the Rayleigh and Log-normal distribution models in fitting with the distribution of snippets intensities. It is shown that the Rayleigh and the Log-normal distribution models followed only with the head of the distribution of the experimental data but not towards the tail of experimental distribution. Further experiment on comparing the backscattering characteristics of snippets data that were collected from different types of seafloor habitats is recommended for future research.
format	Thesis
qualification_level	Master's degree
author	Lau, Kum Weng
author_facet	Lau, Kum Weng
author_sort	Lau, Kum Weng
title	Classification of multibeam snippets data using statistical analysis method
title_short	Classification of multibeam snippets data using statistical analysis method
title_full	Classification of multibeam snippets data using statistical analysis method
title_fullStr	Classification of multibeam snippets data using statistical analysis method
title_full_unstemmed	Classification of multibeam snippets data using statistical analysis method
title_sort	classification of multibeam snippets data using statistical analysis method
granting_institution	Universiti Teknologi Malaysia, Faculty of Geoinformation and Real Estate
granting_department	Faculty of Geoinformation and Real Estate
publishDate	2012
url	http://eprints.utm.my/id/eprint/34588/5/LauKumWengMFGHT2012.pdf
_version_	1747816210996658176

Classification of multibeam snippets data using statistical analysis method

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