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\begin{document}

\title{Deriving Surface Resistivity from Polarimetric SAR Data Using Dual-Input
UNet}



\author[1]{Bibin Wilson }%
\author[1]{Rajiv Kumar }%
\author[1]{Narayanarao Bhogapurapu }%
\author[1]{Anand Singh }%
\author[1]{Amit Sethi }%
\affil[1]{Affiliation not available}%


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  \date{June 14, 2024}


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\begin{abstract}
Traditional survey methods for finding surface re- sistivity are time
consuming and labor intensive. Very few studies have focused on finding
the resistivity / conductivity using remote sensing data and deep
learning techniques. In this line of work, we assessed the correlation
between surface resistivity and Synthetic Aperture Radar (SAR) by
applying various deep learning methods and tested our hypothesis in the
Coso Geothermal Area, USA. For detecting the resistivity, L- band full
polarimetric SAR data acquired by UAVSAR were used, and MT
(Magnetotellurics) inverted resistivity data of the area were used as
the ground truth. We conducted experiments to compare various deep
learning architectures, and suggest the use of Dual Input UNet (DI-UNet)
architecture. DI-UNet uses a deep learning architecture to predict the
resistivity using full polarimetric SAR data by promising a quick survey
addition to the traditional method. Our proposed approach accomplished
improved outcomes for the mapping of MT resistivity from SAR data.%
\end{abstract}%



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