Per-pixel and object-oriented classification methods for mapping urban land cover extraction using SPOT 5 imagery
To have sustainable management and proper decision-making, timely acquisition and analysis of surface features are necessary. Traditional pixel-based analysis is the popular way to extract different categories, but it is not comparable by the achievements that can be achieved through the object-base...
| Main Authors: | , , , |
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| Format: | Article |
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Taylor & Francis
2014
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| Online Access: | http://psasir.upm.edu.my/id/eprint/34884/ |
| _version_ | 1848847898337345536 |
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| author | Jebur, Mustafa Neamah Mohd Shafri, Helmi Zulhaidi Pradhan, Biswajeet Tehrany, Mahyat Shafapour |
| author_facet | Jebur, Mustafa Neamah Mohd Shafri, Helmi Zulhaidi Pradhan, Biswajeet Tehrany, Mahyat Shafapour |
| author_sort | Jebur, Mustafa Neamah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | To have sustainable management and proper decision-making, timely acquisition and analysis of surface features are necessary. Traditional pixel-based analysis is the popular way to extract different categories, but it is not comparable by the achievements that can be achieved through the object-based method that uses the additional characteristics of features in the process of classification. In this paper, three types of classification were used to classify SPOT 5 satellite image in mapping land cover; Support vector machine (SVM) pixel-based, SVM object-based and Decision Tree (DT) pixel-based classification. Normalised Difference Vegetation Index and the brightness value of two infrared bands (NIR and SWIR) were used in manually developed DT classification. The classification of the SVM (pixel based) was generated using the selected groups of pixels that represent the selected features. In addition, the SVM (object based) was implemented by using radial-based function kernel. The classified features were oil palm, rubber, urban area, soil, water and other vegetation. The study found that the overall classification of the DT was the lowest at 69.87% while those of SVM (pixel based) and SVM (object based) were 76.67 and 81.25%, respectively. |
| first_indexed | 2025-11-15T09:25:55Z |
| format | Article |
| id | upm-34884 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T09:25:55Z |
| publishDate | 2014 |
| publisher | Taylor & Francis |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-348842015-12-23T07:17:47Z http://psasir.upm.edu.my/id/eprint/34884/ Per-pixel and object-oriented classification methods for mapping urban land cover extraction using SPOT 5 imagery Jebur, Mustafa Neamah Mohd Shafri, Helmi Zulhaidi Pradhan, Biswajeet Tehrany, Mahyat Shafapour To have sustainable management and proper decision-making, timely acquisition and analysis of surface features are necessary. Traditional pixel-based analysis is the popular way to extract different categories, but it is not comparable by the achievements that can be achieved through the object-based method that uses the additional characteristics of features in the process of classification. In this paper, three types of classification were used to classify SPOT 5 satellite image in mapping land cover; Support vector machine (SVM) pixel-based, SVM object-based and Decision Tree (DT) pixel-based classification. Normalised Difference Vegetation Index and the brightness value of two infrared bands (NIR and SWIR) were used in manually developed DT classification. The classification of the SVM (pixel based) was generated using the selected groups of pixels that represent the selected features. In addition, the SVM (object based) was implemented by using radial-based function kernel. The classified features were oil palm, rubber, urban area, soil, water and other vegetation. The study found that the overall classification of the DT was the lowest at 69.87% while those of SVM (pixel based) and SVM (object based) were 76.67 and 81.25%, respectively. Taylor & Francis 2014 Article PeerReviewed Jebur, Mustafa Neamah and Mohd Shafri, Helmi Zulhaidi and Pradhan, Biswajeet and Tehrany, Mahyat Shafapour (2014) Per-pixel and object-oriented classification methods for mapping urban land cover extraction using SPOT 5 imagery. Geocarto International, 29 (7). 792-806. ISSN 1010-6049; ESSN: 1752-0762 http://www.tandfonline.com/doi/abs/10.1080/10106049.2013.848944 10.1080/10106049.2013.848944 |
| spellingShingle | Jebur, Mustafa Neamah Mohd Shafri, Helmi Zulhaidi Pradhan, Biswajeet Tehrany, Mahyat Shafapour Per-pixel and object-oriented classification methods for mapping urban land cover extraction using SPOT 5 imagery |
| title | Per-pixel and object-oriented classification methods for mapping urban land cover extraction using SPOT 5 imagery |
| title_full | Per-pixel and object-oriented classification methods for mapping urban land cover extraction using SPOT 5 imagery |
| title_fullStr | Per-pixel and object-oriented classification methods for mapping urban land cover extraction using SPOT 5 imagery |
| title_full_unstemmed | Per-pixel and object-oriented classification methods for mapping urban land cover extraction using SPOT 5 imagery |
| title_short | Per-pixel and object-oriented classification methods for mapping urban land cover extraction using SPOT 5 imagery |
| title_sort | per-pixel and object-oriented classification methods for mapping urban land cover extraction using spot 5 imagery |
| url | http://psasir.upm.edu.my/id/eprint/34884/ http://psasir.upm.edu.my/id/eprint/34884/ http://psasir.upm.edu.my/id/eprint/34884/ |