Road Segmentation in High-Resolution Images Using Deep Residual Networks
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 t...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
D. G. Pylarinos
2022-12-01
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| Series: | Engineering, Technology & Applied Science Research |
| Subjects: | |
| Online Access: | https://www.etasr.com/index.php/ETASR/article/view/5247 |
| _version_ | 1797980620501024768 |
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| author | D. Patil S. Jadhav |
| author_facet | D. Patil S. Jadhav |
| author_sort | D. Patil |
| collection | DOAJ |
| description |
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.
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| first_indexed | 2024-04-11T05:58:04Z |
| format | Article |
| id | doaj.art-e8ad7ec8484946879c5cbcd068830d10 |
| institution | Directory Open Access Journal |
| issn | 2241-4487 1792-8036 |
| language | English |
| last_indexed | 2024-04-11T05:58:04Z |
| publishDate | 2022-12-01 |
| publisher | D. G. Pylarinos |
| record_format | Article |
| series | Engineering, Technology & Applied Science Research |
| spelling | doaj.art-e8ad7ec8484946879c5cbcd068830d102022-12-22T04:41:50ZengD. G. PylarinosEngineering, Technology & Applied Science Research2241-44871792-80362022-12-0112610.48084/etasr.5247Road Segmentation in High-Resolution Images Using Deep Residual NetworksD. Patil0S. Jadhav1Department of Electronics and Telecommunication, D. Y. Patil College of Engineering, India | Department of Electronics and Telecommunication, Army Institute of Technology, IndiaDepartment of Information Technology, Army Institute of Technology, India 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. https://www.etasr.com/index.php/ETASR/article/view/5247U-Networkresidual blockencoderdecoder |
| spellingShingle | D. Patil S. Jadhav Road Segmentation in High-Resolution Images Using Deep Residual Networks Engineering, Technology & Applied Science Research U-Network residual block encoder decoder |
| title | Road Segmentation in High-Resolution Images Using Deep Residual Networks |
| title_full | Road Segmentation in High-Resolution Images Using Deep Residual Networks |
| title_fullStr | Road Segmentation in High-Resolution Images Using Deep Residual Networks |
| title_full_unstemmed | Road Segmentation in High-Resolution Images Using Deep Residual Networks |
| title_short | Road Segmentation in High-Resolution Images Using Deep Residual Networks |
| title_sort | road segmentation in high resolution images using deep residual networks |
| topic | U-Network residual block encoder decoder |
| url | https://www.etasr.com/index.php/ETASR/article/view/5247 |
| work_keys_str_mv | AT dpatil roadsegmentationinhighresolutionimagesusingdeepresidualnetworks AT sjadhav roadsegmentationinhighresolutionimagesusingdeepresidualnetworks |