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Machine Learning Framework for Mapping of Mississippi River Levees and Damage Assessment Using Terrasar-X Data

Dabbiru, L., Aanstoos, J.V., Ball, J. E., & Younan, N. H. (2018). Machine Learning Framework for Mapping of Mississippi River Levees and Damage Assessment Using Terrasar-X Data. IEEE Geoscience and Remote Sensing Society (IGARSS), July 2018. Velencia, Spain.

Abstract

Earthen levees protect large areas of populated and cultivated land in the United States. Unstable slope conditions can lead to slump slides, which weaken the levees and increase the likelihood of failure during floods. Such slides could lead to further erosion and through seepage during high water events. Currently, levee inspections are performed infrequently and some of the problem areas are not visible. There is a need to develop cost-effective large-scale methods of screening levees in a timely manner. Sensing the condition of levees remotely can help levee managers to focus and prioritize their inspection and maintenance activities. This paper presents results of applying the TerraSAR-X synthetic aperture radar data to detect vulnerabilities on Francis, Mississippi river levees. In this study, texture features were computed using the discrete wavelet transform (DWT), and both supervised and unsupervised classifiers were tested. The supervised method tested is the SVM (support vector machine), and the unsupervised one is the RXD (Reed-Xiaoli Detector). Both algorithms achieved high accuracy detection of potential slump slides in the test area.


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