Better Estimated IEM Input Parameters Using Random Fractal Geometry Applied on Multi-Frequency SAR Data
AbstractMicrowave 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
Share & Cite This Article
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.
Ghafouri A, Amini J, Dehmollaian M, Kavoosi MA. Better Estimated IEM Input Parameters Using Random Fractal Geometry Applied on Multi-Frequency SAR Data. Remote Sensing. 2017; 9(5):445.Chicago/Turabian Style
Ghafouri, Ali; Amini, Jalal; Dehmollaian, Mojtaba; Kavoosi, Mohammad A. 2017. "Better Estimated IEM Input Parameters Using Random Fractal Geometry Applied on Multi-Frequency SAR Data." Remote Sens. 9, no. 5: 445.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.