This paper explores anomaly detection algorithms
to detect vulnerabilities on Mississippi river levees using remotely
sensed Synthetic Aperture Radar (SAR) data. Earthen levees
protect large areas of populated and cultivated land in the United
States. One sign of potential levee failure is the occurrence of
landslides due to slope instabilities. Such slides could lead to
further erosion and through seepage during high water events.
This research seeks to design a system that is capable of
performing automated target recognition tasks using radar data
to detect problem areas on earthen levees. Polarimetric SAR data
is effective for detecting such phenomena. In this research, we
analyze the ability of different polarization channels in detecting
landslides with different frequency bands of synthetic aperture
radar data using anomaly detection algorithms. The two SAR
datasets used in this study are: (1) the X-band satellite-based
radar data from DLR’s TerraSAR-X satellite, and (2) the L-band
airborne radar data from NASA’s Uninhabited Aerial Vehicle
Synthetic Aperture Radar (UAVSAR). The RX anomaly
detector, an unsupervised classification algorithm, was
implemented to detect anomalies on the levee. The discrete
wavelet transform (DWT) is used for feature extraction. The
algorithm was tested with both the L-band and X-band SAR data
and the results demonstrate that landslide detection using Lband
radar data has better accuracy compared to the X-band
data based on the detection of true positives.