Use of remote sensing to screen earthen levees

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

doi:10.1109/aipr.2010.5759704

Cited by 16 publications

(16 citation statements)

References 8 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%

“…Our motivation for this paper is to compare the performance analysis of several supervised classification methods. SAR technology, due to its high spatial resolution and soil penetration capability, is a good choice to identify such problem areas on levees, so that they can be treated to avoid possible catastrophic failure [1,3]. The roughness and related textural characteristics of the soil in a slide affect the amount and pattern of radar backscatter.…”

Section: Introductionmentioning

confidence: 99%

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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%

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 work is an extension of the previous work [4,15,19,20] of several of the authors. The Reed-Xiaoli (RX) anomaly detector, a training-free unsupervised classification algorithm, was implemented on the levee segments to detect slump slides on the levee [4].…”

Section: Introductionmentioning

confidence: 58%

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

et al. 2017

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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.

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“…There are more than 150,000 kilometres of levee structure with different designs and conditions over the entire US. Therefore, monitoring the levee system in order to detect and classify the levee vulnerabilities can help levee boards and federal agencies to repair them rapidly with lower costs than traditional methods which can cost many millions of dollars (Aanstoos, 2010). One of the problems which can lead to a complete failure during a high water event is a slough slide.…”

Section: Introductionmentioning

confidence: 99%

On Land Slide Detection Using Terrasar-X Over Earthen Levees

Mahrooghy

Aanstoos²,

Prasad³

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Earthen levees have an important role to protect large areas of inhabited and cultivated land in the US from flooding. Failure of the levees can threaten the loss of life and property. One of the problems which can lead to a complete failure during a high water event is a slough slide. In this research, we are trying to detect such slides using X-band SAR data. Our methodology consists of the following four steps: 1) segmentation of the levee area from background; 2) extracting features including backscatter features and texture features; 3) training a back propagation neural network classifier using ground-truth data; and 4) testing the area of interest and validation of the results using ground truth data. A dual-polarimetric X-band image is acquired from the German TerraSAR-X satellite. Ground-truth data include the slides and healthy area. The study area is an approximately 1 km stretch of levee along the lower Mississippi River in the United States. The output classification shows the two classes of healthy and slide areas. The results show classification accuracies of approximately 67% for detecting the slide pixels.

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Use of remote sensing to screen earthen levees

Cited by 16 publications

References 8 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

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

On Land Slide Detection Using Terrasar-X Over Earthen Levees

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