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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis
2016
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| Online Access: | http://psasir.upm.edu.my/id/eprint/63004/1/Road%20condition%20assessment%20by%20OBIA.pdf |
| _version_ | 1825932545964900352 |
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| author | Shahi, Kaveh Mohd Shafri, Helmi Zulhaidi Hamedianfar, Alireza |
| author_facet | Shahi, Kaveh Mohd Shafri, Helmi Zulhaidi Hamedianfar, Alireza |
| author_sort | Shahi, Kaveh |
| collection | UPM |
| 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. |
| first_indexed | 2024-03-06T09:43:41Z |
| format | Article |
| id | upm.eprints-63004 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T09:43:41Z |
| publishDate | 2016 |
| publisher | Taylor & Francis |
| record_format | dspace |
| spelling | 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 |
| 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 |
| title | 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_short | 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 |
| url | http://psasir.upm.edu.my/id/eprint/63004/1/Road%20condition%20assessment%20by%20OBIA.pdf |
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