26 May 2018 Improved discrimination of geological units via geomorphological classification of synthetic aperture radar images
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Abstract
Geological mapping usually requires field visits, but the processing and interpretation of hyperspectral satellite images can also be very beneficial for this task. Microwave or radar remote sensing can obtain surface morphologies using synthetic aperture radar (SAR) images and reduce the duration of field visits dramatically by discriminating geological units based on lithology and texture. This requires the surface roughness to be modeled against microwave signal backscattering. The integral equation model (IEM) is the most well-known rough scattering model, in which surface roughness is calculated using the roughness height statistical parameter (rms-height); however, this study uses an improved IEM based on power-law geometry. The roughness map of the Anaran anticline (located between Dehloran and Ilam in Iran) using TerraSAR images is computed and classified to generate a morphological map. Calculating the roughness map requires training the IEM model and the formation of the look-up-table for pure lithological sites. In situ microtopography measurement was performed on seven different sites containing the main lithologies in the study area, using total station surveying, to train the mathematical model and compare and evaluate the results. Comparing this roughness map with ground-truth data at test sites indicates that computations using the new IEM model results in a misclassification of <10  %   of the samples in the map. This error is acceptable, indicating that the new model could, in many cases, reduce the duration of field visits.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Ali Ghafouri, Ali Ghafouri, Jalal Amini, Jalal Amini, Mojtaba Dehmollaian, Mojtaba Dehmollaian, Mohammad Ali Kavoosi, Mohammad Ali Kavoosi, } "Improved discrimination of geological units via geomorphological classification of synthetic aperture radar images," Journal of Applied Remote Sensing 12(2), 026022 (26 May 2018). https://doi.org/10.1117/1.JRS.12.026022 . Submission: Received: 31 December 2017; Accepted: 7 May 2018
Received: 31 December 2017; Accepted: 7 May 2018; Published: 26 May 2018
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