  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.
