Rapid Damage Detection Using Texture Analysis with Neural Network
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-61287-9_28
Reference22 articles.
1. Bai, Y., et al.: A framework of rapid regional tsunami damage recognition from post-event TerraSAR-X imagery using deep neural networks. IEEE Geosci. Remote Sens. Lett. 15(1), 43–47 (2018). https://doi.org/10.1109/lgrs.2017.2772349
2. Bhangale, U., Durbha, S., Potnis, A., Shinde, R.: Rapid earthquake damage detection using deep learning from VHR remote sensing images. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019) (2019). https://doi.org/10.1109/igarss.2019.8898147
3. Dorafshan, S., Thomas, R.J., Maguire, M.: Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete. Constr. Build. Mater. 186, 1031–1045 (2018). https://doi.org/10.1016/j.conbuildmat.2018.08.011
4. Fan, X., Nie, G., Deng, Y., An, J., Zhou, J., Li, H.: Rapid detection of earthquake damage areas using VIIRS nearly constant contrast night-time light data. Int. J. Remote Sens. 1–24 (2018). https://doi.org/10.1080/01431161.2018.1460512
5. Fujita, A., Sakurada, K., Imaizumi, T., Ito, R., Hikosaka, S., Nakamura, R.: Damage detection from aerial images via convolutional neural networks. In: 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) (2017). https://doi.org/10.23919/mva.2017.7986759
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