Object based image analysis of support vector machine and rule based image classification for building extraction/ Nazatul Asyikin Arham

Building extraction is one of the main procedures used in updating digital maps and geographic information system databases. This is a challenging task in a remote sensing community to extract buildings from high spatial remote sensing imagery because of the spectral similarity between man-made obje...

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Main Author:	Arham, Nazatul Asyikin
Format:	Thesis
Language:	English
Published:	2020
Subjects:	Analysis Image processing
Online Access:	https://ir.uitm.edu.my/id/eprint/34565/1/34565.pdf
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id	my-uitm-ir.34565
record_format	uketd_dc
spelling	my-uitm-ir.345652022-12-06T07:59:58Z Object based image analysis of support vector machine and rule based image classification for building extraction/ Nazatul Asyikin Arham 2020 Arham, Nazatul Asyikin Analysis Image processing Building extraction is one of the main procedures used in updating digital maps and geographic information system databases. This is a challenging task in a remote sensing community to extract buildings from high spatial remote sensing imagery because of the spectral similarity between man-made objects such as buildings, parking lots, roads, in the urban areas. This study utilizes Pleiades-1A satellite image data of Shah Alam areas to extract buildings in urban area. The main goal of this study is to demonstrate the capability of object-based image analysis (OBIA) in building extraction from high spatial remote sensing imagery. Different classification approaches, including support vector machine (SVM) and rule-based classification, were applied to the Pleiades-1 A. Results show that rule-based classification has a better overall accuracy closeness index with 0.07 while SVM had 0.14 of overall accuracy closeness index. The rule-based classification resulted in fewer buildings that under-segmentation and over-segmentation. The classification accuracy of the result obtained is approximately 95% for SVM and 83% for rule-based classification. The overall accuracy and kappa coefficient for SVM is 95.11% and 93% respectively and the classification accuracy using rule-based image classification shows 83.49%) and 76%) of overall accuracy and kappa coefficient respectively. The map of building extraction using SVM shows the distribution of building, tree, road, waterbody, land, grass and shadow area are 14%, 19%, 23%, 6%, 12%, 26%, and 0% respectively and the map of building extraction using rule-based image classification shows 26%), 24%o, 14%), 3%o, 30%), 3%) and 0% of building, grass, land, road, tree, waterbody and shadow area respectively. 2020 Thesis https://ir.uitm.edu.my/id/eprint/34565/ https://ir.uitm.edu.my/id/eprint/34565/1/34565.pdf text en public degree Universiti Teknologi MARA (UiTM) Faculty of Architecture, Planning and Surveying Khalid, Nafisah (Dr.)
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Khalid, Nafisah (Dr.)
topic	Analysis Image processing
spellingShingle	Analysis Image processing Arham, Nazatul Asyikin Object based image analysis of support vector machine and rule based image classification for building extraction/ Nazatul Asyikin Arham
description	Building extraction is one of the main procedures used in updating digital maps and geographic information system databases. This is a challenging task in a remote sensing community to extract buildings from high spatial remote sensing imagery because of the spectral similarity between man-made objects such as buildings, parking lots, roads, in the urban areas. This study utilizes Pleiades-1A satellite image data of Shah Alam areas to extract buildings in urban area. The main goal of this study is to demonstrate the capability of object-based image analysis (OBIA) in building extraction from high spatial remote sensing imagery. Different classification approaches, including support vector machine (SVM) and rule-based classification, were applied to the Pleiades-1 A. Results show that rule-based classification has a better overall accuracy closeness index with 0.07 while SVM had 0.14 of overall accuracy closeness index. The rule-based classification resulted in fewer buildings that under-segmentation and over-segmentation. The classification accuracy of the result obtained is approximately 95% for SVM and 83% for rule-based classification. The overall accuracy and kappa coefficient for SVM is 95.11% and 93% respectively and the classification accuracy using rule-based image classification shows 83.49%) and 76%) of overall accuracy and kappa coefficient respectively. The map of building extraction using SVM shows the distribution of building, tree, road, waterbody, land, grass and shadow area are 14%, 19%, 23%, 6%, 12%, 26%, and 0% respectively and the map of building extraction using rule-based image classification shows 26%), 24%o, 14%), 3%o, 30%), 3%) and 0% of building, grass, land, road, tree, waterbody and shadow area respectively.
format	Thesis
qualification_level	Bachelor degree
author	Arham, Nazatul Asyikin
author_facet	Arham, Nazatul Asyikin
author_sort	Arham, Nazatul Asyikin
title	Object based image analysis of support vector machine and rule based image classification for building extraction/ Nazatul Asyikin Arham
title_short	Object based image analysis of support vector machine and rule based image classification for building extraction/ Nazatul Asyikin Arham
title_full	Object based image analysis of support vector machine and rule based image classification for building extraction/ Nazatul Asyikin Arham
title_fullStr	Object based image analysis of support vector machine and rule based image classification for building extraction/ Nazatul Asyikin Arham
title_full_unstemmed	Object based image analysis of support vector machine and rule based image classification for building extraction/ Nazatul Asyikin Arham
title_sort	object based image analysis of support vector machine and rule based image classification for building extraction/ nazatul asyikin arham
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Architecture, Planning and Surveying
publishDate	2020
url	https://ir.uitm.edu.my/id/eprint/34565/1/34565.pdf
_version_	1783734271689621504

Object based image analysis of support vector machine and rule based image classification for building extraction/ Nazatul Asyikin Arham

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