Building extraction of worldview3 imagery via support vector machine using scikit-learn module / Najihah Ismail

Building is one of the major features that available on the land used on the earth especially in urban area. By using remote sensing method, it can reduce time to collect data for a large area. The data can be gain in high or low resolution. Low resolution image is cheaper and easy to access meanwhi...

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Bibliographic Details
Main Author:	Ismail, Najihah
Format:	Thesis
Language:	English
Published:	2021
Subjects:	Remote Sensing Land use
Online Access:	https://ir.uitm.edu.my/id/eprint/42992/1/42992.pdf
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id	my-uitm-ir.42992
record_format	uketd_dc
spelling	my-uitm-ir.429922021-03-10T01:09:07Z Building extraction of worldview3 imagery via support vector machine using scikit-learn module / Najihah Ismail 2021-03-05 Ismail, Najihah Remote Sensing Land use Building is one of the major features that available on the land used on the earth especially in urban area. By using remote sensing method, it can reduce time to collect data for a large area. The data can be gain in high or low resolution. Low resolution image is cheaper and easy to access meanwhile high resolution can provide a better view and more accurate to differentiate the features available on image. Nowadays, researchers have investigated the use of different approach for building classification and extraction. However, there is need to monitor the effectiveness of the method used effectively. Consequently, this study is intending to apply Support Vector Machine (SVM) classification which using Scikit-learn module for building classification. Moreover, the capability of the programming based using python for building extraction can be assessed. Python is an open source of programming software that conducted programming-based technique using the Scikit-Learn module to do the extraction of building from Land used land cover (LULC) and the result was 86.233% for overall accuracy. A Commercial Remote Sensing Technology (ENVI) was used and measured to improve and verify the performance of the Python programming-based picture classification by applying the same SVM algorithm and the tests indicated 95.0732% for an overall accuracy. 2021-03 Thesis https://ir.uitm.edu.my/id/eprint/42992/ https://ir.uitm.edu.my/id/eprint/42992/1/42992.pdf text en public degree Universiti Teknologi Mara Perlis Faculty of Architecture, Planning and Surveying
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Remote Sensing Land use
spellingShingle	Remote Sensing Land use Ismail, Najihah Building extraction of worldview3 imagery via support vector machine using scikit-learn module / Najihah Ismail
description	Building is one of the major features that available on the land used on the earth especially in urban area. By using remote sensing method, it can reduce time to collect data for a large area. The data can be gain in high or low resolution. Low resolution image is cheaper and easy to access meanwhile high resolution can provide a better view and more accurate to differentiate the features available on image. Nowadays, researchers have investigated the use of different approach for building classification and extraction. However, there is need to monitor the effectiveness of the method used effectively. Consequently, this study is intending to apply Support Vector Machine (SVM) classification which using Scikit-learn module for building classification. Moreover, the capability of the programming based using python for building extraction can be assessed. Python is an open source of programming software that conducted programming-based technique using the Scikit-Learn module to do the extraction of building from Land used land cover (LULC) and the result was 86.233% for overall accuracy. A Commercial Remote Sensing Technology (ENVI) was used and measured to improve and verify the performance of the Python programming-based picture classification by applying the same SVM algorithm and the tests indicated 95.0732% for an overall accuracy.
format	Thesis
qualification_level	Bachelor degree
author	Ismail, Najihah
author_facet	Ismail, Najihah
author_sort	Ismail, Najihah
title	Building extraction of worldview3 imagery via support vector machine using scikit-learn module / Najihah Ismail
title_short	Building extraction of worldview3 imagery via support vector machine using scikit-learn module / Najihah Ismail
title_full	Building extraction of worldview3 imagery via support vector machine using scikit-learn module / Najihah Ismail
title_fullStr	Building extraction of worldview3 imagery via support vector machine using scikit-learn module / Najihah Ismail
title_full_unstemmed	Building extraction of worldview3 imagery via support vector machine using scikit-learn module / Najihah Ismail
title_sort	building extraction of worldview3 imagery via support vector machine using scikit-learn module / najihah ismail
granting_institution	Universiti Teknologi Mara Perlis
granting_department	Faculty of Architecture, Planning and Surveying
publishDate	2021
url	https://ir.uitm.edu.my/id/eprint/42992/1/42992.pdf
_version_	1783734678499360768

Building extraction of worldview3 imagery via support vector machine using scikit-learn module / Najihah Ismail

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