UAVSAR: A New NASA Airborne SAR System for Science and Technology Research

Rosen, P. A.; Hensley, S.; Wheeler, K.; Sadowy, G.; Miller, Theodore K.; Shaffer, S.; Muellerschoen, Ron; Jones, Cathleen E.; Zebker, H. A.; Madsen, S.N.

doi:10.1109/radar.2006.1631770

Cited by 86 publications

(61 citation statements)

References 4 publications

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“…UAVSAR is specifically designed to acquire airborne repeat track SAR data for differential interferometric measurements [26]. The single look complex (SLC) data from UAVSAR is of very high resolution with an azimuth slant pixel spacing of 0.6 m and range pixel spacing of 1.6 m. However, the single-look images are very speckled in slant range, which makes visual interpretation and characterization very difficult.…”

Section: Methodsmentioning

confidence: 99%

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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Section: Methodsmentioning

confidence: 99%

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

et al. 2017

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“…UAVSAR is a fully polarimetric L-band sensor designed for acquiring airborne repeat-track interferometry SAR data [39]. Its applications include monitoring ground deformations, ice dynamics, volcano dynamics, local sea ice dynamics, time-varying evaporation and hydraulic properties of soils, and aboveground biomass [40].…”

Section: Methodsmentioning

confidence: 99%

Improved van Zyl Polarimetric Decomposition Lessening the Overestimation of Volume Scattering Power

2014

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This paper improves van Zyl's Nonnegative Eigenvalue Decomposition (NNED). Orientation angle compensation and helix scattering are introduced to the decomposition. The volume scattering parameters that explain the most cross-polarized power are selected. If volume scattering and helix scattering explain all cross-polarized power in the measured coherency matrix, then simply perform van Zyl decomposition to the remainder matrix; otherwise, the measured coherency matrix is decomposed into three components, i.e., helix scattering, volume scattering, and one ground scattering. The latter two scattering are all modeled by Neumann's adaptive depolarizing model, according to which some cross-polarized power is attributed to ground scattering hence the orientation angle randomness of volume scattering and the dominant ground scattering are obtained. In this way, all cross-polarized power could be well explained. Experiments using UAVSAR data showed that more than 99.8% of total pixels are well fitted. Negative power is avoided. Compared with van Zyl decomposition, volume scattering power is reduced by up to 8.73% on average. The given volume scattering power is often lower than that by three latest NNED.

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“…The UAVSAR radar has a range bandwidth of 80 MHz which gives it a 1.8 m range resolution (Rosen et al, 2006). The dataset contains 699 by 1237 pixels selected from agricultural area near Winnipeg, in Manitoba, Canada, during Jane and July in 2012 (See Figure 3.a and 3.b).…”

Section: Description Of Datasetmentioning

confidence: 99%

Unsupervised Change Detection in Sar Images Using Gaussian Mixture Models

Kiana

Homayouni

Sharifi

et al. 2015

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

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ABSTRACT:In this paper, we propose a method for unsupervised change detection in Remote Sensing Synthetic Aperture Radar (SAR) images. This method is based on the mixture modelling of the histogram of difference image. In this process, the difference image is classified into three classes; negative change class, positive change class and no change class. However the SAR images suffer from speckle noise, the proposed method is able to map the changes without speckle filtering. To evaluate the performance of this method, two dates of SAR data acquired by Uninhabited Aerial Vehicle Synthetic from an agriculture area are used. Change detection results show better efficiency when compared to the state-of-the-art methods.

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UAVSAR: A New NASA Airborne SAR System for Science and Technology Research

Cited by 86 publications

References 4 publications

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

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

Improved van Zyl Polarimetric Decomposition Lessening the Overestimation of Volume Scattering Power

Unsupervised Change Detection in Sar Images Using Gaussian Mixture Models

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