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Electronics 2017, 6(2), 29; doi:10.3390/electronics6020029

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

1
Geosystems Research Institute, Mississippi State University, 2 Research Boulevard, Starkville, MS 39759, USA
2
Electrical and Computer Engineering, Mississippi State University, 406 Hardy Road, Mississippi State, MS 39762, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Mostafa Bassiouni
Received: 31 December 2016 / Revised: 22 March 2017 / Accepted: 28 March 2017 / Published: 31 March 2017
(This article belongs to the Special Issue Radio and Radar Signal Processing)
View Full-Text   |   Download PDF [3884 KB, uploaded 31 March 2017]   |  

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. View Full-Text
Keywords: discrete wavelet transform (DWT); gray level co-occurrence matrix (GLCM); image classification; radar polarimetry; synthetic aperture radar (SAR); support vector machine (SVM) discrete wavelet transform (DWT); gray level co-occurrence matrix (GLCM); image classification; radar polarimetry; synthetic aperture radar (SAR); support vector machine (SVM)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Dabbiru, L.; Aanstoos, J.V.; Ball, J.E.; Younan, N.H. Screening Mississippi River Levees Using Texture-Based and Polarimetric-Based Features from Synthetic Aperture Radar Data. Electronics 2017, 6, 29.

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