Earthen levee monitoring with Synthetic Aperture Radar

Aanstoos, James V.; Hasan, Khaled; O’Hara, Charles G.; Prasad, Saurabh; Dabbiru, Lalitha; Mahrooghy, Majid; Gokaraju, Balakrishna; Nóbrega, Rodrigo Affonso de Albuquerque

doi:10.1109/aipr.2011.6176370

Cited by 12 publications

(11 citation statements)

References 9 publications

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“…On-site inspection of levees is costly and time-consuming, so there is a need to develop efficient techniques based on remote sensing technologies to identify levees that are more vulnerable to failure under climate change (higher loads) and overloading of water levels (risk, flood loading) [1][2][3]. One type of problem that occurs along these levees which can lead to complete failure during a high-water event are slough slides [1][2][3][4] (also called landslides or slump slides). Slough slides are slope failures along a levee, which leave areas of the levee vulnerable to seepage and failure during high water events.…”

Section: Introductionmentioning

confidence: 99%

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

2017

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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.

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Section: Introductionmentioning

confidence: 99%

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

2017

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“…Interferometric SAR has been successfully used for monitoring changes in elevation in urban deltaic regions in the New Orleans area (Dixon et al 2006). In alluvial settings, recent SAR research has focused on using pattern recognition technology and soil moisture to assess levee condition and distress (Aanstoos et al 2011(Aanstoos et al , 2012a(Aanstoos et al , 2012b. These studies have focused on evaluating shallow slides caused by changes in soil moisture.…”

Section: Remote Monitoring and Inspectionmentioning

confidence: 99%

“…Remote monitoring for shallow slides L-band SAR and satellite remote sensing using commercial imagery have been used to screen for shallow levee slides along the Mississippi River in the USACE Vicksburg District (Aanstoos et al 2011(Aanstoos et al , 2012a(Aanstoos et al , 2012bHossain et al 2006). These techniques are typically performed in the dry season due to soil moisture issues using radar and/or during leaf-off season for imagery based media.…”

Section: 67mentioning

confidence: 99%

Remote sensing and monitoring of earthen flood-control structures

Dunbar¹,

Galan-Comas²,

Walshire³

et al. 2017

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The purpose of this study was to identify and review technologies that are applicable in locating weaknesses and poor performance within floodcontrol structures from extreme loading events. The focus of this study was to assess current technologies and state-of-practice techniques involving remote sensing, testing, and real-time monitoring of earthen structures. Advancements in satellite and sensor technology combined with high-speed Internet and telecommunication capabilities and smart decision-making software permit real-time monitoring of earthen floodcontrol structures such as dams and levees.Technologies evaluated included both active and passive sensing methods. These technologies included satellite, airborne, and ground-based sensor systems to identify surface and subsurface characteristics of the watershed, as well as point sensors typically embedded in hydraulic structures to monitor the health of the structure. Point sensors typically record water loading, soil pore pressures, soil movements, and other important properties to evaluate global stability of the water control structure. Geophysical-based methods are typically used in mapping, monitoring, and detection of subsurface stratigraphy, seepage, and any changes in subsurface conditions through time within flood-control structures and their foundations.

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“…We tested these features using different sliding window sizes (5,7,8, and 9) and mother wavelets (Daubechies, Haar, Symlet, and Biorthogonal) and selected those that maximize the separation of the targets of interest from the background. The Daubechies mother wavelet was chosen, and a window size of 7.…”

Section: B Textural Featuresmentioning

confidence: 99%

Detection of slump slides on earthen levees using polarimetric SAR imagery

Aanstoos

Hasan

O’Hara

et al. 2012

2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

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Abstract-Key results are presented of an extensive project studying the use of synthetic aperture radar (SAR) as an aid to the levee screening process. SAR sensors used are: (1) The NASA UAVSAR (Uninhabited Aerial Vehicle SAR), a fully polarimetric L-band SAR capable of sub-meter ground sample distance; and (2) The German TerraSAR-X radar satellite, also multi-polarized and featuring 1-meter GSD, but using an X-band carrier. The study area is a stretch of 230 km of levees along the lower Mississippi River. The L-band measurements can penetrate vegetation and soil somewhat, thus carrying some information on soil texture and moisture which are relevant features to identifying levee vulnerability to slump slides. While X-band does not penetrate as much, its ready availability via satellite makes multitemporal algorithms practical. Various feature types and classification algorithms were applied to the polarimetry data in the project; this paper reports the results of using the Support Vector Machine (SVM) and back-propagation Artificial Neural Network (ANN) classifiers with a combination of the polarimetric backscatter magnitudes and texture features based on the wavelet transform. Ground reference data used to assess classifier performance is based on soil moisture measurements, soil sample tests, and on site visual inspections.

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Earthen levee monitoring with Synthetic Aperture Radar

Cited by 12 publications

References 9 publications

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

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

Remote sensing and monitoring of earthen flood-control structures

Detection of slump slides on earthen levees using polarimetric SAR imagery

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