Damaged Road Extraction Based on Simulated Post-Disaster Remote Sensing Images
Published in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021
Damaged road extraction is a challenging task in the field of remote sensing. Some existing methods include the step to extract road from pre- and post-disaster remote sensing images of the same area. In practice, it often occurs that one of these two images is missing. To solve this problem, we use CoCosNet, the model for exemplar-based image translation, to translate pre-disaster images to simulated post-disaster ones. Then we use D-LinkNet, the state-of-the-art method in road extraction, to extract road from the pre- and post-disaster images of the same area. We extract damaged road area by comparing pre-disaster road masks with post-disaster ones and output the damage level by calculating the proportion of the damaged road area. Finally, we evaluate the damaged road extraction accuracy. Experimental results on simulated post-disaster images prove the effectiveness of the simulation method and the framework for damaged road extraction and damage level evaluation.
Recommended citation: Y. Huang, H. Wei, J. Yang and M. Wu, "Damaged Road Extraction Based on Simulated Post-Disaster Remote Sensing Images," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, pp. 4684-4687
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