Abstract
Flood extent mapping plays a crucial role in disaster management and national water forecasting. In recent years, high-resolution optical imagery becomes increasingly available with the deployment of numerous small satellites and drones. However, analyzing such imagery data to extract flood extent poses unique challenges due to the rich noise and shadows, obstacles (e.g., tree canopies, clouds), and spectral confusion between pixel classes (flood, dry) due to spatial heterogeneity. Existing machine learning techniques often focus on spectral and spatial features from raster images without fully incorporating the geographic terrain within classification models. In contrast, we recently proposed a novel machine learning model called geographical hidden Markov tree that integrates spectral features of pixels and topographic constraints from Digital Elevation Model (DEM) data (i.e., water flow directions) in a holistic manner. This paper evaluates the model through case studies on high-resolution aerial imagery from the National Oceanic and Atmospheric Administration (NOAA) National Geodetic Survey together with DEM. Three scenes are selected in heavily vegetated floodplains near the cities of Grimesland and Kinston in North Carolina during Hurricane Matthew floods in 2016. Results show that the proposed hidden Markov tree model outperforms several state of the art machine learning algorithms (e.g., random forests, gradient boosted model) by an improvement of F-score (the harmonic mean of the user's accuracy and producer's accuracy) from around 70% to 80% to over 95% on our datasets.
Abstract (translated)
URL
https://arxiv.org/abs/2008.11230
