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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Published inGeocarto international Vol. 32; no. 12; pp. 1389 - 1406
Main Authors Shahi, Kaveh, Shafri, Helmi Zulhaidi Mohd, Hamedianfar, Alireza
Format Journal Article
LanguageEnglish
Published Taylor & Francis 02.12.2017
Subjects
bitumen
chi-square
color
economic development
feature selection
image analysis
Object-based image analysis (OBIA)
remote sensing
road condition
roads
support vector machines
WorldView-2
Online AccessGet full text
ISSN1010-6049
1752-0762
1752-0762
DOI10.1080/10106049.2016.1213888

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Abstract 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.
AbstractList 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.
Author Shafri, Helmi Zulhaidi Mohd
Hamedianfar, Alireza
Shahi, Kaveh
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  givenname: Alireza
  surname: Hamedianfar
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Snippet Accurate information on the conditions of road asphalt is necessary for economic development and transportation management. In this study, object-based image...
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SubjectTerms bitumen
chi-square
color
economic development
feature selection
image analysis
Object-based image analysis (OBIA)
remote sensing
road condition
roads
support vector machines
WorldView-2
Title Road condition assessment by OBIA and feature selection techniques using very high-resolution WorldView-2 imagery
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