Sentinel-1 SAR and LiDAR to detect extent and depth flood using Random Forests machine learning.
IEEE International Geoscience and Remote Sensing Symposium (IGARSS)(2022)
Abstract
This research was carried out to identify the extent and depth of flooded areas using Sentinel-1 SAR, the Digital Elevation Model generated with LiDAR and Random Forest machine learning. Training and cross-validation was performed on a set of backscatter value samples obtained from Sentinel-1. The results indicate that out of five combinations, the Random Forest algorithm had the best performance when using the four combinations (RF + Polarization VH+VV + MDE) with F1m = 0.977, AUC = 0.998 and Kappa = 0.955. SAR images have potential advantages that allow rapid and efficient diagnosis of the extent of flooding caused by excess rainfall in many regions around world.
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Key words
Flooding, Sentinel-1 SAR, Random Forest Machine Learning
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