Flood Mapping Using UAVSAR and Convolutional Neural Networks

Michael Denbina, Zaid J. Towfic, Matthew Thill, Brian D. Bue, Neda Kasraee, Annemarie Peacock, Yunling Lou

Abstract: We have mapped flooded areas in data collected by the NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) using two convolutional neural network (CNN) image classifier architectures: U-Net and SegNet. Our study area was a region around Houston, TX, USA affected by widespread flooding in 2017 due to Hurricane Harvey. To train and test the classifiers, we manually labelled over 10000 image segments in two flight lines. Both U-Net and SegNet yielded higher accuracy than a previous non-machine learning classifier we used as a baseline. U-Net had slightly higher accuracy than SegNet. The classifiers performed better in areas with more homogeneous land cover. To independently validate the classifier accuracy we used NOAA aerial imagery, with overall accuracy around 80%. Future work includes assessing the classifier robustness in other study areas, assessing the classifier dependence on UAVSAR incidence angle, particularly for open water and bare ground, and collecting more training data, particularly in urban areas. This study demonstrates the potential of CNN image classifiers for mapping flooded areas in airborne polarimetric SAR imagery, and for land cover classification of polarimetric SAR imagery more generally.

External IDs:dblp:conf/igarss/DenbinaTTBKPL20