Detection of Slump Slides on Earthen Levees Using Polarimetric SAR Imagery
Aanstoos, J.V., Hasan, K., O\'Hara C., Dabbiru, L., Mahrooghy, M., Nobrega, R. A. A., & Lee, M. (2012). Detection of Slump Slides on Earthen Levees Using Polarimetric SAR Imagery. 2012 IEEE Applied Imagery Pattern Recognition Workshop. Washington, DC: IEEE.
Earthen levees protect large areas of populated and cultivated land in the US from flooding. Across the entire US, there are over 150,000 kilometers of levee structures. Currently, there are limited processes in place to prioritize the monitoring these structures. Managers of both private and government-owned levees need to assess their condition rapidly to identify, classify, and prioritize vulnerabilities so that costly and time-consuming inspections can be focused on areas of greatest need.
This paper presents results of an extensive project studying the use of synthetic aperture radar (SAR) as an aid to the levee screening process. SAR sensors being utilized include:
(1) The NASA UAVSAR (Uninhabited Aerial Vehicle SAR), a fully polarimetric L-band SAR which is specifically designed to acquire airborne repeat track SAR data for differential interferometric measurements. The instrument is capable of sub-meter ground sample distance.
(2) The German TerraSAR-X radar satellite, also multi-polarized and featuring 1-meter GSD, but using an X-band carrier.
Our test study area is a stretch of 230 km of levees along the lower Mississippi River.
The L-band measurements can penetrate soil somewhat, thus carrying some information on soil texture and moisture which are relevant features to identifying levee vulnerability to slump slides. While X-band does not penetrate as much, its ready availability via satellite makes multitemporal algorithms practical. Our results compare relative accuracy, advantages, and disadvantages of each.
We report on the use of various feature detection algorithms being applied to the polarimetry data, including entropy-anisotropy decomposition, wavelet transforms, and methods based on the Grey Level Co-occurrence Matrix (GLCM). The features detected are compared with various ground truth data including soil conductivity measurements, soil sample tests, and on site visual inspections.