An innovative approach to detect anomalies on earthen levees using unsupervised classification of polarimetric SAR imagery

Abstract

The dynamics of surface and subsurface water events can lead to slope instability resulting in slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We used L-band synthetic aperture radar (SAR) to screen levees for anomalies. Using Entropy (H), Anisotropy (A), and alpha (α) parameters, we implemented several unsupervised classification algorithms for the identification of anomalies on the levee. The classification techniques applied are: H/α, H/A, A/α, wishart H/α, and wishart H/A/α classification. The effectiveness of the algorithms is demonstrated using quad-polarimetric L-band SAR imagery from the NASA Jet Propulsion Laboratory's (JPL's) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the southern USA, where earthen flood control levees are maintained by the US Army Corps of Engineers.