Measuring the surface roughness of geological rock surfaces in SAR data using fractal geometry

Ghafouri, Ali; Amini, Jalal; Dehmollaian, Mojtaba; Kavoosi, Mohammad Ali

doi:10.1016/j.crte.2017.04.003

Cited by 15 publications

(9 citation statements)

References 25 publications

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“…What is needed to discriminate geological formations according to their morphology is the surface roughness [25]. In this section, the results of the IEM implementation to simulate the backscattering coefficient (σ • ) using the surface data and a comparison with the SAR measured ones in the radar bands L, C, and X for the surface roughness computation is described (Figure 1a).…”

Section: Evaluation Of Backscattering and Surface Roughness Simulationmentioning

confidence: 99%

Section: Evaluation Of Backscattering and Surface Roughness Simulationmentioning

confidence: 99%

“…Precisely being a point on the diagonal line of each graph indicates that the IEM simulated value on the corresponding pixel is exactly equal to the measured backscattering on that pixel. Therefore, in these graphs, the farness of the diagonal line shows the simulation error [25,37]. Backscattering simulation accuracy via the IEM for the geological formations on the L-band, (a) using the conventional geometry and the exponential ACF; (b) using the conventional geometry and the Gaussian ACF; (c) using the Power-Law geometry for the calculation of the IEM input parameters.…”

Section: Evaluation Of Backscattering and Surface Roughness Simulationmentioning

confidence: 99%

“…Moreover, W (n) is another geometric term in the IEM, i.e., the Fourier transform of the nth power of the surface ACF, A(x), either its exponential, or Gaussian regression [24,25] (i.e., respectively (7)-(9)):…”

Section: Conventional Inputs For Iemmentioning

confidence: 99%

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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 Of Backscattering and Surface Roughness Simulationmentioning

confidence: 99%

Section: Evaluation Of Backscattering and Surface Roughness Simulationmentioning

confidence: 99%

Section: Evaluation Of Backscattering and Surface Roughness Simulationmentioning

confidence: 99%

Section: Conventional Inputs For Iemmentioning

confidence: 99%

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

et al. 2017

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“…According to the actual land surface roughness computation by the radar data, there are a wide variety of methods are known, both classical physical simulation [14] and popular modern approaches, for example -the use of fractal geometry [15] or neural networks [16].…”

Section: State Of the Artmentioning

confidence: 99%

Оцінка Параметрів Шорсткості Земної Поверхні За Двохполяризаційним Радіолокаційним Відбиттям Методом Зворотного Моделювання

2023

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Radar remote sensing is a modern and advantageous method for aerospace research of the Earth. The mass commissioning of new high-resolution radar systems based on synthetic aperture radar (SAR) has greatly expanded the capabilities of radar imaging. The expediency of the SAR using was approved in domains that traditionally used remote sensing data, such as cartography, agriculture and forestry, mineral prospecting, environmental security, disaster monitoring, defense.The primary physical parameter of the land surface, which is registered by non-interferometric SAR, is the radar backscattering coefficient (sigma nought). The received radar signal is a source for the complex simulation of backscattering processes and evaluation of the secondary land surface physical and biophysical characteristics: texture, soil moisture, dielectric permittivity, vegetation cover structure, etc. Herewith the land surface roughness, described by the standard deviation of its vertical irregularities, is one of the most significant entities in any radar simulation. Therefore, the land surface roughness restoration using radar remote sensing data is a high-relevant task. The land surface roughness is an independent physical parameter, in much determining the radar backscattering. However, the roughness correlation length depends on the polarization of the radar signal.This paper describes a quantitative approach to the land surface roughness recovery by dual-polarization SAR imagery using separate measurements adjustment of independent physical value -dielectric permittivity in different polarizations. The proposed Baghdadi et al. semi-empirical calibration of IEM radar backscattering taking into account the polarization-dependent correlation length is used to ensure the physical equivalence of the land surface dielectric permittivity measurements in different polarizations. In addition, the paper provides the required computational equations, as well as the example of an actual Sentinel-1 radar image processing. The results obtained are generally correspond to the known physical patterns and landscape features of the study area.

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Weakening of mudstone fragments due to disintegration: an experimental investigation

Shen

Tang

Wang

et al. 2020

Bull Eng Geol Environ

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Measuring the surface roughness of geological rock surfaces in SAR data using fractal geometry

Cited by 15 publications

References 25 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

Оцінка Параметрів Шорсткості Земної Поверхні За Двохполяризаційним Радіолокаційним Відбиттям Методом Зворотного Моделювання

Weakening of mudstone fragments due to disintegration: an experimental investigation

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