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Road condition assessment by OBIA and feature selection techniques using very high-resolution WorldView-2 imagery

Accurate information on the conditions of road asphalt is necessary for economic development and transportation management. In this study, object-based image analysis (OBIA) rule-sets are proposed based on feature selection technique to extract road asphalt conditions (good and poor) using WorldView...

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Main Authors: Shahi, Kaveh, Mohd Shafri, Helmi Zulhaidi, Hamedianfar, Alireza
Format: Article
Language:English
Published: Taylor & Francis 2016
Online Access:http://psasir.upm.edu.my/id/eprint/63004/1/Road%20condition%20assessment%20by%20OBIA.pdf
http://psasir.upm.edu.my/id/eprint/63004/
https://www.tandfonline.com/doi/abs/10.1080/10106049.2016.1213888
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spelling my.upm.eprints.630042018-08-27T09:34:17Z http://psasir.upm.edu.my/id/eprint/63004/ Road condition assessment by OBIA and feature selection techniques using very high-resolution WorldView-2 imagery Shahi, Kaveh Mohd Shafri, Helmi Zulhaidi Hamedianfar, Alireza Accurate information on the conditions of road asphalt is necessary for economic development and transportation management. In this study, object-based image analysis (OBIA) rule-sets are proposed based on feature selection technique to extract road asphalt conditions (good and poor) using WorldView-2 (WV-2) satellite data. Different feature selection techniques, including support vector machine (SVM), random forest (RF) and chi-square (CHI) are evaluated to indicate the most effective algorithm to identify the best set of OBIA attributes (spatial, spectral, textural and colour). The chi-square algorithm outperformed SVM and RF techniques. The classification result based on CHI algorithm achieved an overall accuracy of 83.19% for the training image (first site). Furthermore, the proposed model was used to examine its performance in different areas; and it achieved accuracy levels of 83.44, 87.80 and 80.26% for the different selected areas. Therefore, the selected method can be potentially useful for detecting road conditions based on WV-2 images. Taylor & Francis 2016-08-02 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/63004/1/Road%20condition%20assessment%20by%20OBIA.pdf Shahi, Kaveh and Mohd Shafri, Helmi Zulhaidi and Hamedianfar, Alireza (2016) Road condition assessment by OBIA and feature selection techniques using very high-resolution WorldView-2 imagery. Geocarto International, 32 (12). 1389 - 1406. ISSN 1010-6049; ESSN: 1752-0762 https://www.tandfonline.com/doi/abs/10.1080/10106049.2016.1213888 10.1080/10106049.2016.1213888
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 Accurate information on the conditions of road asphalt is necessary for economic development and transportation management. In this study, object-based image analysis (OBIA) rule-sets are proposed based on feature selection technique to extract road asphalt conditions (good and poor) using WorldView-2 (WV-2) satellite data. Different feature selection techniques, including support vector machine (SVM), random forest (RF) and chi-square (CHI) are evaluated to indicate the most effective algorithm to identify the best set of OBIA attributes (spatial, spectral, textural and colour). The chi-square algorithm outperformed SVM and RF techniques. The classification result based on CHI algorithm achieved an overall accuracy of 83.19% for the training image (first site). Furthermore, the proposed model was used to examine its performance in different areas; and it achieved accuracy levels of 83.44, 87.80 and 80.26% for the different selected areas. Therefore, the selected method can be potentially useful for detecting road conditions based on WV-2 images.
format Article
author Shahi, Kaveh
Mohd Shafri, Helmi Zulhaidi
Hamedianfar, Alireza
spellingShingle Shahi, Kaveh
Mohd Shafri, Helmi Zulhaidi
Hamedianfar, Alireza
Road condition assessment by OBIA and feature selection techniques using very high-resolution WorldView-2 imagery
author_facet Shahi, Kaveh
Mohd Shafri, Helmi Zulhaidi
Hamedianfar, Alireza
author_sort Shahi, Kaveh
title Road condition assessment by OBIA and feature selection techniques using very high-resolution WorldView-2 imagery
title_short Road condition assessment by OBIA and feature selection techniques using very high-resolution WorldView-2 imagery
title_full Road condition assessment by OBIA and feature selection techniques using very high-resolution WorldView-2 imagery
title_fullStr Road condition assessment by OBIA and feature selection techniques using very high-resolution WorldView-2 imagery
title_full_unstemmed Road condition assessment by OBIA and feature selection techniques using very high-resolution WorldView-2 imagery
title_sort road condition assessment by obia and feature selection techniques using very high-resolution worldview-2 imagery
publisher Taylor & Francis
publishDate 2016
url http://psasir.upm.edu.my/id/eprint/63004/1/Road%20condition%20assessment%20by%20OBIA.pdf
http://psasir.upm.edu.my/id/eprint/63004/
https://www.tandfonline.com/doi/abs/10.1080/10106049.2016.1213888
_version_ 1643837729374470144
score 13.211869

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