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Remote Sens. 2017, 9(5), 445;

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

School of Surveying and Geospatial Engineering, Faculty of Engineering, University of Tehran, Tehran 1439957131, Iran
Center of Excellence on Applied Electromagnetic Systems, School of Electrical and Computer Engineering, University of Tehran, Tehran 1439957131, Iran
Department of Geology, Exploration Directorate of National Iranian Oil Company, Tehran 1994814695, Iran
Author to whom correspondence should be addressed.
Academic Editors: Timo Balz, Uwe Soergel, Mattia Crespi, Batuhan Osmanoglu, Nicolas Baghdadi and Prasad Thenkabail
Received: 26 February 2017 / Revised: 2 May 2017 / Accepted: 4 May 2017 / Published: 5 May 2017
(This article belongs to the Special Issue Advances in SAR: Sensors, Methodologies, and Applications)
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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. View Full-Text
Keywords: Synthetic Aperture Radar (SAR); Integral Equation Model (IEM); random fractal geometry Synthetic Aperture Radar (SAR); Integral Equation Model (IEM); random fractal geometry

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Ghafouri, A.; Amini, J.; Dehmollaian, M.; Kavoosi, M.A. Better Estimated IEM Input Parameters Using Random Fractal Geometry Applied on Multi-Frequency SAR Data. Remote Sens. 2017, 9, 445.

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