Polarimetric Radar Imaging

Lee, Jong-Sen; Pottier, Éric

doi:10.1201/9781420054989

Cited by 946 publications

(401 citation statements)

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“…2), which is called the "backscatter alignment" (BSA) convention (Lüneburg and Boerner, 2004, Lee and Pottier, 2009, Sect. 3.1.3, or Cloude, 2010.…”

Section: Sign Of Cpd In Backscatter Alignment Conventionmentioning

confidence: 99%

Anisotropy of seasonal snow measured by polarimetric phase differences in radar time series

Leinß

Löwe²,

Proksch³

et al. 2016

The Cryosphere

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Abstract. The snow microstructure, i.e., the spatial distribution of ice and pores, generally shows an anisotropy which is driven by gravity and temperature gradients and commonly determined from stereology or computer tomography. This structural anisotropy induces anisotropic mechanical, thermal, and dielectric properties. We present a method based on radio-wave birefringence to determine the depth-averaged, dielectric anisotropy of seasonal snow with radar instruments from space, air, or ground. For known snow depth and density, the birefringence allows determination of the dielectric anisotropy by measuring the copolar phase difference (CPD) between linearly polarized microwaves propagating obliquely through the snowpack. The dielectric and structural anisotropy are linked by MaxwellGarnett-type mixing formulas. The anisotropy evolution of a natural snowpack in Northern Finland was observed over four winters (2009)(2010)(2011)(2012)(2013) with the ground-based radar instrument "SnowScat". The radar measurements indicate horizontal structures for fresh snow and vertical structures in old snow which is confirmed by computer tomographic in situ measurements. The temporal evolution of the CPD agreed in ground-based data compared to space-borne measurements from the satellite TerraSAR-X. The presented dataset provides a valuable basis for the development of new snow metamorphism models which include the anisotropy of the snow microstructure.

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“…2), which is called the "backscatter alignment" (BSA) convention (Lüneburg and Boerner, 2004, Lee and Pottier, 2009, Sect. 3.1.3, or Cloude, 2010.…”

Section: Sign Of Cpd In Backscatter Alignment Conventionmentioning

confidence: 99%

Anisotropy of seasonal snow measured by polarimetric phase differences in radar time series

Leinß

Löwe²,

Proksch³

et al. 2016

The Cryosphere

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“…It is well known that the scattering matrix in CP-basis can be linked to that in LP-basis via transformation [3]:…”

Section: Ct1 Cp Calibration Processmentioning

confidence: 99%

“…Pol-SAR systems exploit polarimetric backscattering behavior of manmade and natural scatters for the derivation of bio-and geo-physical information of targets such as land, snow and ice, ocean, vegetation, and urban area [1][2][3][4]. In addition to its traditional use in airborne and spaceborne SAR systems, Pol-SAR is also increasingly used in various ground-based (GB) SAR applications such as vegetation monitoring, damage assessment of wooden building structure, and buried target detection [5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

The Development and Comparison of Two Polarimetric Calibration Techniques for Ground-Based Circularly Polarized Radar System

Izumi

Demirci

Baharuddin³

et al. 2017

PIER B

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Abstract-Two techniques are described for the calibration of ground-based (GB) circularly polarized (CP) full polarimetric radars. The techniques are based on the point target calibration approach that uses various types of canonical reflectors with different orientations. Specifically, the calibration methods for linearly polarized (LP) radar proposed by Wiesbeck et al. and Gau et al. are selected and adapted to CP with suitable reflectors. The applicability of the techniques is examined through C-band scatterometric and synthetic aperture radar (SAR) measurements in an anechoic chamber. For the scatterometric mode, comparisons of calibrated channel imbalances with theoretical values show agreement within ±0.3 dB in amplitude and ±5 • in phase. The crosstalk between the channels is also reduced by ∼ 5 to 30 dB after calibration. For the SAR mode, calibrated scattering matrix of a vertical wire target exhibits significant elimination of distortions between channel amplitudes and phases. The effect of calibration on target parameter retrieval is also investigated through the Cloud-Pottier eigenvector-based decomposition. Both calibration techniques are shown to yield improved accuracy of entropy-alpha (H-ᾱ) distributions and orientation angle (β) values.

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“…Synthetic Aperture Radar (SAR) image databases have an important role in target recognition and identification system development (Lee 1980;Rihaczek and Hershkowitz 2000;Margarit et al 2006;Kasim et al 2008;Lee and Pottier 2009;Guo et al 2010;Huang and Lee 2010;Tian et al 2011;Wang et al 2011). It is mainly dependent on the operator's prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

SAR Image Simulations Using the LBM Algorithm on MPI-GPU

Sun¹,

Tsai²,

Chiang³

2016

Terr. Atmos. Ocean. Sci.

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Synthetic Aperture Radar (SAR) is a powerful tool for studying natural environments under all-weather and day-andnight conditions. SAR system design and data-processing algorithm simulation is noted for its controllable parameters. The satellite SAR echo signal simulation framework has been successfully applied to target recognition based on Radarsat-2 and TerraSAR-X images and in strip map mode. However, such SAR image simulation works only on CPU or GPU (graphics processing units) and requires huge calculations. We developed a "Load-Balancing Model (LBM)" algorithm that uses Message Passing Interface GPU (MPI-GPU) to reduce the inner loop load and improve the computational performance. The LBM algorithm uses MPI-GPU technology to build the simple GPU cluster system. The LBM algorithm is used to separate the intensive computing and controlling tasks for each node, and exploit the contemporary GPU computation capability to accelerate the computing tasks. We conducted a relevant experiment on a target radar cross section (RCS) and improved the performance by a factor of > 40 compared to a 4-core CPU accelerated program.

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Polarimetric Radar Imaging

Cited by 946 publications

References 0 publications

Anisotropy of seasonal snow measured by polarimetric phase differences in radar time series

Anisotropy of seasonal snow measured by polarimetric phase differences in radar time series

The Development and Comparison of Two Polarimetric Calibration Techniques for Ground-Based Circularly Polarized Radar System

SAR Image Simulations Using the LBM Algorithm on MPI-GPU

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