A Fractal Algorithm for Surface Roughness Parameters retrieval using Multi -Band/polarization AIRSAR Data
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Surface roughness is an important characteristic in analyzing SAR images and three-dimensional (3D) surfaces that influence the backscattering of electromagnetic waves. This paper proposes an algorithm based on a fractal method for retrieving parameters of a surface. To estimate surface roughness parameters, 10000 different 3D surfaces with different rms- height and correlation length are simulated. 3D simulation of surfaces is carried out based on fractal Brownian motion (fBm). The results show that the fractal method represents a good relationship between roughness parameters and fractal dimension. It can be seen that, in some cases, surfaces with different roughness have the same fractal dimension. Roughness Index (RI) can be used as a complement to fractal dimension. This paper presents new empirical relationships between fractal dimension, roughness parameters, and roughness index. The method is implemented on L and C bands with HH and HV Polarizations of AIRSAR data at different dates. The comparison between field measurement and estimated roughness showed that the accuracy of soil surface roughness estimation for band L with HH polarization is better than band L and C with polarization HV and HH respectively. The results of this method also are compared with the roughness estimates using the Integral Equation Model (IEM). The analysis of outputs shows that the roughness estimation using the fractal and IEM are very similar in the low moisture at L band in HH polarization. The RMSE of roughness for data at L band in HH polarization on July 1, 2002 is 0.54 and 0.53 cm for fractal and IEM respectively.