Discrimination of Geological Top-Formations by their Morphology through SAR Images and via Fractal Geometry implementation in IEM Backscattering Model(Case Study: Zagros Thrust Belt)
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Abstract:
Morphological discrimination of geological top-formations is the supplemental procedure of geological mapping; so in situ measurements to register geomorphological data are unavoidable; though due to the impassable and fault cliffs field operations to visit all areas within a geological map is almost impossible. Microwave or radar remote sensing, via synthetic aperture radar (SAR) images is capable to obtain the surface morphology and alteration zones discrimination on the basis of lithology texture. For this purpose, it is necessary to model the surface roughness against microwave signal backscattering; among available models, Integral Equation Model (IEM) is the most famous one, in which surface roughness is calculable via roughness height statistical parameter (RMS-height). Whereas, this parameter is not capable enough to measure, since it measures the surface roughness merely in vertical direction, and roughness dispersion on the surface is not included. To apply the proposed method of geomorphological mapping, the roughness map for the area of concern which is the northern part of Anaran anticline (located between Dehloran and Ilam cities in Iran) using ALOS-PALSAR and TerraSAR images is computed. Field micro-topography measurement is performed on three different sites containing the main lithologies of the case study, using surveying total station. It is clarified in comparison of roughness map with the ground truth, that using fractal geometry parameters in IEM model computation, the standard deviation had more than 10% of decrease, in comparison with conventional IEM calculations. In addition, in this paper, a comparison is made between the results obtained with another article from the authors to the results gained by the method of this article, which shows a 10 to 15 percent advantage of this paper method.
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Journal title
volume 7 issue 3
pages 137- 157
publication date 2019-12
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