An Update on the IEM Surface Backscattering Model

Fung, A.K.; Chen, K.S.

doi:10.1109/lgrs.2004.826564

Cited by 114 publications

(72 citation statements)

References 4 publications

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“…2017, 9, 445 3 of 16 The IEM calculated and the SAR measured backscattering coefficients are mutually compared to evaluate the methodology (Figure 1a). Additionally, comparing the in situ rms-height with the inverse IEM results, the level of efficacy when using the power-law inputs versus the conventional ones can be cleared [12,15] (Figure 1b).…”

Section: Evaluation Inputsmentioning

confidence: 99%

Better Estimated IEM Input Parameters Using Random Fractal Geometry Applied on Multi-Frequency SAR Data

et al. 2017

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Abstract:Microwave remote sensing can measure surface geometry. Via the processing of the Synthetic Aperture Radar (SAR) data, the earth surface geometric parameters can be provided for geoscientific studies, especially in geological mapping. For this purpose, it is necessary to model the surface roughness against microwave signal backscattering. Of the available models, the Integral Equation Model (IEM) for co-polarized data has been the most frequently used model. Therefore, by the processing of the SAR data using this model, the surface geometry can be studied. In the IEM, the surface roughness geometry is calculable via the height statistical parameter, the rms-height. However, this parameter is not capable enough to represent surface morphology, since it only measures the surface roughness in the vertical direction, while the roughness dispersion on the surface is not included. In this paper, using the random fractal geometry capability, via the implementation of the power-law roughness spectrum, the precision and correctness of the surface roughness estimation has been improved by up to 10%. Therefore, the random fractal geometry is implemented through the calculation of the input geometric parameters of the IEM using the power-law surface spectrum and the spectral slope. In this paper, the in situ roughness measurement data, as well as SAR images at frequencies of L, C, and X, have been used to implement and evaluate the proposed method. Surface roughness, according to the operational frequencies, exhibits a fractal or a diffractal behavior.

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Section: Evaluation Inputsmentioning

confidence: 99%

Better Estimated IEM Input Parameters Using Random Fractal Geometry Applied on Multi-Frequency SAR Data

et al. 2017

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“…where K = − jk 4πR exp(−jkR) Corresponding to the Kirchhoff and the complementary surface fields in (6a), (6b), the far-zone scattered field may also be expressed as the sum of the Kirchhoff and complementary scattered fields [3,7]: (16) where the Kirchhoff field is given by…”

Section: Far-zone Scattered Field and Scattering Coefficientsmentioning

confidence: 99%

“…Driven by the need of predicting bistatic scattering and microwave emissivity, much effort has been devoted to further improving the IEM accuracy [9][10][11][12][13][14][15][16][17][18][19] by removing some of the assumptions originally imposed for the purpose of mathematical simplicity during the course of derivation. Another leap forward step was the introduction of a transition function into the Fresnel reflection coefficients to take spatial dependence into account, removing the restrictions on the limits of surface roughness and permittivity [3,11].…”

Section: Introductionmentioning

confidence: 99%

Extension and Validation of an Advanced Integral Equation Model for Bistatic Scattering From Rough Surfaces

Chen¹,

Chen²,

Li³

et al. 2015

PIER

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Abstract-This paper deals with the modeling of bistatic scattering from a randomly rough surface. An advanced integral equation model is presented by giving its general framework of model developments, model expressions, and predictions of bistatic scattering for various surface parameters. Extension work to improve the model accuracy is also reported in more detail. In particular, the transition function for the Fresnel reflection coefficient is in more general form. Model predictions are illustrated, demonstrated, and validated by extensive comparisons with numerical simulations. The updated advanced integral equation model remains a compact algebraic form for single scattering and substantially improves prediction accuracy in bistatic scattering that is drawing more emerging applications in earth remote sensing.

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“…Backscattering coefficients for different material surfaces can be measured or estimated with existing models such as Kirchhoff method or IEM [2]. Each frequency sample is integrated over a time equal to the reciprocal of a low-pass filter cutoff frequency .…”

Section: A Emission From Surfacementioning

confidence: 99%

Radar Interferometric Measurements With a Planar Patch Antenna Array

Nilsson

Bååth²

2007

IEEE Sensors J.

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Abstract-A planar patch antenna array has been made for radar interferometry. The antenna array consists of 32 rectangular patches on a ceramic loaded teflon substrate. The patches are individually coupled to the microwave electronics in two orthogonal circular polarizations. The radar interferometer is intended for topographic imaging in industrial environment, in this case, a blast furnace producing hot metal for the steel and metal industry. Ordinary imaging techniques with IR or visible radiation are not possible to use due to high temperatures, scattering from dust and particles. A model of blast furnace burden material surface was measured and detected.

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An Update on the IEM Surface Backscattering Model

Cited by 114 publications

References 4 publications

Better Estimated IEM Input Parameters Using Random Fractal Geometry Applied on Multi-Frequency SAR Data

Better Estimated IEM Input Parameters Using Random Fractal Geometry Applied on Multi-Frequency SAR Data

Extension and Validation of an Advanced Integral Equation Model for Bistatic Scattering From Rough Surfaces

Radar Interferometric Measurements With a Planar Patch Antenna Array

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