Screening Mississippi River Levees Using Texture-Based and Polarimetric-Based Features from Synthetic Aperture Radar Data

Lalitha Dabbiru,James Aanstoos,Nicolas Younan,John Ball

doi:10.3390/electronics6020029

Lalitha Dabbiru, James Aanstoos + Show 2 more

Open Access

https://doi.org/10.3390/electronics6020029

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Journal: Electronics	Publication Date: Mar 31, 2017
Citations: 3	License type: CC BY 4.0

Affiliation: Mississippi State University

Abstract

This article reviews the use of synthetic aperture radar remote sensing data for earthen levee mapping with an emphasis on finding the slump slides on the levees. Earthen levees built on the natural levees parallel to the river channel are designed to protect large areas of populated and cultivated land in the Unites States from flooding. One of the signs of potential impending levee failure is the appearance of slump slides. On-site inspection of levees is expensive and time-consuming; therefore, a need to develop efficient techniques based on remote sensing technologies is mandatory to prevent failures under flood loading. Analysis of multi-polarized radar data is one of the viable tools for detecting the problem areas on the levees. In this study, we develop methods to detect anomalies on the levee, such as slump slides and give levee managers new tools to prioritize their tasks. This paper presents results of applying the National Aeronautics and Space Administration (NASA) Jet Propulsion Lab (JPL)’s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) quad-polarized L-band data to detect slump slides on earthen levees. The study area encompasses a portion of levees of the lower Mississippi River in the United States. In this paper, we investigate the performance of polarimetric and texture features for efficient levee classification. Texture features derived from the gray level co-occurrence (GLCM) matrix and discrete wavelet transform were computed and analyzed for efficient levee classification. The pixel-based polarimetric decomposition features, such as entropy, anisotropy, and scattering angle were also computed and applied to the support vector machine classifier to characterize the radar imagery and compared the results with texture-based classification. Our experimental results showed that inclusion of textural features derived from the SAR data using the discrete wavelet transform (DWT) features and GLCM features provided higher overall classification accuracies compared to the pixel-based polarimetric features.

Highlights

There are over 100,000 miles of dam and levee structures of varying designs and conditions in the United States
The support vector machine (SVM) was utilized to classify the results for the discrete wavelet transform (DWT), gray level co-occurrence matrix (GLCM), and Polarimetric features
The texture features from DWT, GLCM and H/A/α polarimetric features were included separately and the SVM algorithm was implemented using a Gaussian radial basis function (RBF) kernel

Summary

Introduction

There are over 100,000 miles of dam and levee structures of varying designs and conditions in the United States. The Levee Board implements strict annual levee maintenance programs that are expensive and require many man hours of the levee personnel to perform inspection These inspections are helpful for identifying the weak areas on the levee; there are limited processes to monitor these structures and predict potential risk to communities. Two types of problems that occur along the levees, which can be precursors to complete failure during a high water event are slough (or slump) slides and sand boils. If the underlying foundation materials that support the levee are weak, or become destabilized, a slope failure can develop and result in a catastrophic failure of the levee These slope failures can form as slough/slump slides along a levee and are vulnerable to levee failure. We focus on slump slides, since they occur in areas more visible to remote sensing

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Results

Conclusion