MediaEval2019: Flood Detection in Time Sequence Satellite Images

Authors

Palavi Jain, Technological University DublinFollow
Bianca Schoen-Phelan, Technological University DublinFollow
Robert J. Ross, Technological University DublinFollow

Document Type

Conference Paper

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

Computer Sciences

Abstract

In this work, we present a flood detection technique from time series satellite images for the City-centered satellite sequences (CCSS) task in the MediaEval 2019 competition [1]. This work utilises a three channel feature indexing technique [13] along with a VGG16 pretrained model for automatic detection of floods. We also compared our result with RGB images and a modified NDWI technique by Mishra et al, 2015 [15]. The result shows that the three channel feature indexing technique performed the best with VGG16 and is a promising approach to detect floods from time series satellite images.

DOI

https://doi.org/10.21427/pj3n-kr08

Recommended Citation

Schoen-Phelan, B. & Ross, R. (2019). MediaEval2019: Flood Detection in Time Sequence Satellite Images. Proceedings of the MediaEval 2019 Workshop, Sophia Antipolis, France, 27-30 October 2019. doi:10.21427/pj3n-kr08

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.