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A rule-based parameter aided with object-based classification approach for extraction of building and roads from WorldView-2 images

Roads and buildings constitute a significant proportion of urban areas. Considerable amount of research has been done on the road and building extraction from remotely sensed imagery. However, a few of them have been concentrating on using only spectral information. This study presents a comparison...

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Main Authors: Ziaei, Zahra, Pradhan, Biswajeet, Mansor, Shattri
Format: Article
Language:English
Published: Taylor & Francis 2014
Online Access:http://psasir.upm.edu.my/id/eprint/36277/1/A%20rule-based%20parameter%20aided%20with%20object-based%20classification%20approach%20for%20extraction%20of%20building%20and%20roads%20from%20WorldView-2%20images.pdf
http://psasir.upm.edu.my/id/eprint/36277/
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spelling my.upm.eprints.362772015-11-11T07:05:24Z http://psasir.upm.edu.my/id/eprint/36277/ A rule-based parameter aided with object-based classification approach for extraction of building and roads from WorldView-2 images Ziaei, Zahra Pradhan, Biswajeet Mansor, Shattri Roads and buildings constitute a significant proportion of urban areas. Considerable amount of research has been done on the road and building extraction from remotely sensed imagery. However, a few of them have been concentrating on using only spectral information. This study presents a comparison between three object-based models for urban features’ classification, specifically roads and buildings, from WorldView-2 satellite imagery. The three applied algorithms are support vector machines (SVMs), nearest neighbour (NN) and proposed rule-based system. The results indicated that the proposed rules in this study, despite the spectral complexity of land cover types, performed a satisfactory output with an overall accuracy of 92.92%. The advantages offered by the proposed rules were not provided by other two applied algorithms and it revealed the highest accuracy compared to SVM and NN. The overall accuracy for SVM was 76.76%, which is almost similar to the result achieved by NN (77.3%). Taylor & Francis 2014 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/36277/1/A%20rule-based%20parameter%20aided%20with%20object-based%20classification%20approach%20for%20extraction%20of%20building%20and%20roads%20from%20WorldView-2%20images.pdf Ziaei, Zahra and Pradhan, Biswajeet and Mansor, Shattri (2014) A rule-based parameter aided with object-based classification approach for extraction of building and roads from WorldView-2 images. Geocarto International, 29 (5). pp. 554-569. ISSN 1010-6049; ESSN: 1752-0762 10.1080/10106049.2013.819039
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Roads and buildings constitute a significant proportion of urban areas. Considerable amount of research has been done on the road and building extraction from remotely sensed imagery. However, a few of them have been concentrating on using only spectral information. This study presents a comparison between three object-based models for urban features’ classification, specifically roads and buildings, from WorldView-2 satellite imagery. The three applied algorithms are support vector machines (SVMs), nearest neighbour (NN) and proposed rule-based system. The results indicated that the proposed rules in this study, despite the spectral complexity of land cover types, performed a satisfactory output with an overall accuracy of 92.92%. The advantages offered by the proposed rules were not provided by other two applied algorithms and it revealed the highest accuracy compared to SVM and NN. The overall accuracy for SVM was 76.76%, which is almost similar to the result achieved by NN (77.3%).
format Article
author Ziaei, Zahra
Pradhan, Biswajeet
Mansor, Shattri
spellingShingle Ziaei, Zahra
Pradhan, Biswajeet
Mansor, Shattri
A rule-based parameter aided with object-based classification approach for extraction of building and roads from WorldView-2 images
author_facet Ziaei, Zahra
Pradhan, Biswajeet
Mansor, Shattri
author_sort Ziaei, Zahra
title A rule-based parameter aided with object-based classification approach for extraction of building and roads from WorldView-2 images
title_short A rule-based parameter aided with object-based classification approach for extraction of building and roads from WorldView-2 images
title_full A rule-based parameter aided with object-based classification approach for extraction of building and roads from WorldView-2 images
title_fullStr A rule-based parameter aided with object-based classification approach for extraction of building and roads from WorldView-2 images
title_full_unstemmed A rule-based parameter aided with object-based classification approach for extraction of building and roads from WorldView-2 images
title_sort rule-based parameter aided with object-based classification approach for extraction of building and roads from worldview-2 images
publisher Taylor & Francis
publishDate 2014
url http://psasir.upm.edu.my/id/eprint/36277/1/A%20rule-based%20parameter%20aided%20with%20object-based%20classification%20approach%20for%20extraction%20of%20building%20and%20roads%20from%20WorldView-2%20images.pdf
http://psasir.upm.edu.my/id/eprint/36277/
_version_ 1643831699843317760
score 13.211869

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