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Road Extraction from High-Resolution Orthophoto Images Using Convolutional Neural Network

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Abstract

Two of the major applications in geospatial information system (GIS) and remote sensing fields are object detection and man-made feature extraction (e.g., road sections) from high-resolution remote sensing imagery. Extracting roads from high-resolution remotely sensed imagery plays a crucial role in multiple applications, such as navigation, emergency tasks, land cover change detection, and updating GIS maps. This study presents a deep learning technique based on a convolutional neural network (CNN) to classify and extract roads from orthophoto images. We applied the model on five orthophoto images to specify the superiority of the method for road extraction. First, we used principal component analysis and object-based image analysis for pre-processing to not only obtain spectral information but also add spatial and textural information for enhancing the classification accuracy. Then, the obtained results from the previous step were used as input for the CNN model to classify the images into road and non-road parts and trivial opening and closing operation are applied to extract connected road components from the images and remove holes inside the road parts. For the accuracy assessment of the proposed method, we used measurement factors such as precision, recall, F1 score, overall accuracy, and IOU. Achieved results showed that the average percentages of these factors were 91.09%, 95.32%, 93.15%, 94.44%, and 87.21%. The results were also compared with those of other existing methods. The comparison ascertained the reliability and superior performance of the suggested model architecture for extracting road regions from orthophoto images.

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Acknowledgements

The authors acknowledge and appreciate the provision of orthophoto images from airborne laser scanning data (LiDAR) by the Department of Planning.

Funding

This research is supported by the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS) in the University of Technology Sydney (UTS) under Grants 321740.2232335 and 321740.2232357.

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Authors and Affiliations

  1. Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney, Ultimo, NSW, 2007, Australia

    Abolfazl Abdollahi, Biswajeet Pradhan & Nagesh Shukla

  2. Center of Excellence for Climate Change Research, King Abdulaziz University, P. O. Box 80234, Jeddah, 21589, Saudi Arabia

    Biswajeet Pradhan

  3. Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia (UKM), 43600, Bangi, Selangor, Malaysia

    Biswajeet Pradhan

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  1. Abolfazl Abdollahi
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Correspondence to Biswajeet Pradhan.

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Abdollahi, A., Pradhan, B. & Shukla, N. Road Extraction from High-Resolution Orthophoto Images Using Convolutional Neural Network. J Indian Soc Remote Sens 49, 569–583 (2021). https://doi.org/10.1007/s12524-020-01228-y

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