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Levee Anomaly Detection Using Synthetic Aperture Radar Imagery

Dabbiru, L., Aanstoos, J.V., Younan, N. H., & Moorhead, R. J. (2016). Levee Anomaly Detection Using Synthetic Aperture Radar Imagery. Naval Oceanographic Office 4th Annual Operational Oceanography Science and Technology Poster Symposium. Stennis Space Center, MS.


The main focus of this research is to detect vulnerabilities on the Mississippi river levees using remotely sensed Synthetic Aperture Radar (SAR) imagery. Unstable slope conditions can lead to slump slides, which weaken the levees and increase the likelihood of failure during floods. On-site inspection of levees is expensive and time-consuming, so there is a need to develop efficient automated techniques based on remote sensing technologies to identify levees that are more vulnerable to failure under flood loading. Synthetic Aperture Radar technology, due to its high spatial resolution and potential soil penetration capability, is a good choice to identify problem areas along the levee so that they can be treated to avoid possible catastrophic failure. This research analyzes the ability of detecting the anomalies on the levee with different frequency bands of SAR data. The two SAR datasets used in this study are: (1) the L-band airborne radar data from NASA JPL’s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), and (2) the X-band satellite-based radar data from DLR’s TerraSAR-X (TSX). The main contribution of this research is the development of a machine learning framework to 1) provide improved knowledge on the status of the levees, 2) detect anomalies on the levee sections, 3) provide early warning of impending levee failures, and 4) develop efficient tools for levee health assessment. Polarimetric and textural features have been computed and utilized in the classification tasks to achieve efficient levee characterization. Various approaches of image analysis methods for characterizing levee segments within the study area have been implemented and tested. The RX anomaly detector, a training-free unsupervised classification algorithm detected the active slump slides on the levee at the time of image acquisition and also flagged some areas as “anomalous”, where new slides appeared at a later date. This technique is very fast and does not depend on ground truth information, so these results guide levee managers to investigate the areas shown as anomalies in the classification map. The support vector machine (SVM) supervised learning algorithm with grey level co-occurrence matrix (GLCM) features provided excellent results in identifying slump slides on the levee.

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