Preprint Concept Paper Version 1 Preserved in Portico This version is not peer-reviewed

A Supervised Classification Method for Levee Slide Detection Using Complex Synthetic Aperture Radar Imagery

Version 1 : Received: 10 August 2016 / Approved: 10 August 2016 / Online: 10 August 2016 (11:37:16 CEST)

A peer-reviewed article of this Preprint also exists.

Marapareddy, R.; Aanstoos, J.V.; Younan, N.H. A Supervised Classification Method for Levee Slide Detection Using Complex Synthetic Aperture Radar Imagery. J. Imaging 2016, 2, 26. Marapareddy, R.; Aanstoos, J.V.; Younan, N.H. A Supervised Classification Method for Levee Slide Detection Using Complex Synthetic Aperture Radar Imagery. J. Imaging 2016, 2, 26.

Abstract

The dynamics of surface and sub-surface water events can lead to slope instability resulting in anomalies such as slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We have implemented a supervised Mahalanobis distance classification algorithm for the detection of slough slides on levees using complex polarimetric Synthetic Aperture Radar (polSAR) data. The classifier output was followed by a spatial majority filter post-processing step which improved the accuracy. The effectiveness of the algorithm is demonstrated using fully quad-polarimetric L-band Synthetic Aperture Radar (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. Slide detection accuracy of up to 98 percent was achieved, although the number of available slides examples was small.

Keywords

Synthetic Aperture Radar; UAVSAR; levee; classification; radar polarimetry; classification

Subject

Engineering, Electrical and Electronic Engineering

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