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Electronics 2017, 6(4), 83; https://doi.org/10.3390/electronics6040083

Accuracy Analysis Comparison of Supervised Classification Methods for Anomaly Detection on Levees Using SAR Imagery

1
School of Computing, University of Southern Mississippi, Hattiesburg, MS 39406, USA
2
Geosystems Research Institute, Mississippi State University, Mississippi State, MS 39759, USA
3
Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA
*
Author to whom correspondence should be addressed.
Received: 28 August 2017 / Revised: 7 October 2017 / Accepted: 12 October 2017 / Published: 14 October 2017
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Abstract

This paper analyzes the use of a synthetic aperture radar (SAR) imagery to support levee condition assessment by detecting potential slide areas in an efficient and cost-effective manner. Levees are prone to a failure in the form of internal erosion within the earthen structure and landslides (also called slough or slump slides). If not repaired, slough slides may lead to levee failures. In this paper, we compare the accuracy of the supervised classification methods minimum distance (MD) using Euclidean and Mahalanobis distance, support vector machine (SVM), and maximum likelihood (ML), using SAR technology to detect slough slides on earthen levees. In this work, the effectiveness of the algorithms was demonstrated using quad-polarimetric L-band SAR imagery from the NASA Jet Propulsion Laboratory’s (JPL’s) uninhabited aerial vehicle synthetic aperture radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the Southern USA, where earthen flood control levees are maintained by the US Army Corps of Engineers. View Full-Text
Keywords: supervised classification; radar polarimetry; earthen levees; radar polarimetry; synthetic aperture radar (SAR) supervised classification; radar polarimetry; earthen levees; radar polarimetry; synthetic aperture radar (SAR)
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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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Marapareddy, R.; Aanstoos, J.V.; Younan, N.H. Accuracy Analysis Comparison of Supervised Classification Methods for Anomaly Detection on Levees Using SAR Imagery. Electronics 2017, 6, 83.

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