Document Type
Conference Paper
Rights
Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence
Disciplines
Information Science, Remote sensing
Abstract
The early and accurate detection of floods from satellite imagery can aid rescue planning and assessment of geophysical damage. Automatic identification of water from satellite images has historically relied on hand-crafted functions, but these often do not provide the accuracy and robustness needed for accurate and early flood detection. To try to overcome these limitations we investigate a tiered methodology combining water index like features with a deep convolutional neural network based solution to flood identification against the MediaEval 2019 flood dataset. Our method builds on existing deep neural network methods, and in particular the VGG16 network. Specifically, we explored different water indexing techniques and proposed a water index function with the use of Green/SWIR and Blue/NIR bands with VGG16. Our experiment shows that our approach outperformed all other water index technique when combined with VGG16 network in order to detect flood in images.
DOI
https://doi.org/10.1145/3341105.3374023
Recommended Citation
Jain, P., Schoen-Phelan, B. and Ross, R. (2020). Automatic flood detection in SentineI-2 images using deep convolutional neural networks. Proceedings of the 35th Annual ACM Symposium on Applied Computing (SAC ’20). Association for Computing Machinery, New York, NY, USA,p.617–623. doi:10.1145/3341105.3374023
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Publication Details
SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing