Road condition assessment by OBIA and feature selection techniques using very high-resolution WorldView-2 imagery |
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| Authors: | Kaveh Shahi Alireza Hamedianfar |
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| Institution: | 1. Faculty of Engineering, Department of Civil Engineering, Universiti Putra Malaysia (UPM), Serdang, Malaysia;2. Department of Surveying Engineering, Islamic Azad University, Estahban, Iran;3. Young Researchers and Elite Club, Islamic Azad University, Estahban, Iran |
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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. |
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| Keywords: | Object-based image analysis (OBIA) WorldView-2 feature selection chi-square road condition |
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