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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| Format: | Thesis |
| Language: | English |
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2020
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| Online Access: | https://ir.uitm.edu.my/id/eprint/34565/ |
| _version_ | 1848808585690087424 |
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| author | Arham, Nazatul Asyikin |
| author_facet | Arham, Nazatul Asyikin |
| author_sort | Arham, Nazatul Asyikin |
| building | UiTM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T23:01:04Z |
| format | Thesis |
| id | uitm-34565 |
| institution | Universiti Teknologi MARA |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T23:01:04Z |
| publishDate | 2020 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uitm-345652022-12-06T07:59:58Z https://ir.uitm.edu.my/id/eprint/34565/ Object based image analysis of support vector machine and rule based image classification for building extraction/ Nazatul Asyikin Arham 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 NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/34565/1/34565.pdf Arham, Nazatul Asyikin (2020) Object based image analysis of support vector machine and rule based image classification for building extraction/ Nazatul Asyikin Arham. (2020) Degree thesis, thesis, Universiti Teknologi MARA (UiTM). |
| 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 |
| title | 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_short | 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 |
| topic | Analysis Image processing |
| url | https://ir.uitm.edu.my/id/eprint/34565/ |