Road Segmentation in High-Resolution Images Using Deep Residual Networks


  • D. Patil Department of Electronics and Telecommunication, D. Y. Patil College of Engineering, India | Department of Electronics and Telecommunication, Army Institute of Technology, India
  • S. Jadhav Department of Information Technology, Army Institute of Technology, India
Volume: 12 | Issue: 6 | Pages: 9654-9660 | December 2022 |


Automatic road detection from remote sensing images is a vital application for traffic management, urban planning, and disaster management. The presence of occlusions like shadows of buildings, trees, and flyovers in high-resolution images and miss-classifications in databases create obstacles in the road detection task. Therefore, an automatic road detection system is required to detect roads in the presence of occlusions. This paper presents a deep convolutional neural network to address the problem of road detection, consisting of an encoder-decoder architecture. The architecture contains a U-Network with residual blocks. U-Network allows the transfer of low-level features to the high-level, helping the network to learn low-level details. Residual blocks help maintain the network's training performance, which may deteriorate due to a deep network. The encoder and decoder structures generate a feature map and classify pixels into road and non-road classes, respectively. Experimentation was performed on the Massachusetts road dataset. The results showed that the proposed model gave better accuracy than current state-of-the-art methods.


U-Network, residual block, encoder, decoder


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How to Cite

D. Patil and S. Jadhav, “Road Segmentation in High-Resolution Images Using Deep Residual Networks”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 6, pp. 9654–9660, Dec. 2022.


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