Authors:

Emiko A Sano

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About Paper:

Unmanned Aerial Vehicles (UAVs) equipped with Synthetic Aperture Radar (SAR) offer a cost-effective and efficient platform for high- resolution remote sensing at microwave frequency. Producing accurately geocoded SAR imagery over complex terrain without distortion, however, is a challenge due to the radar's side-looking observation geometry, which can cause layover, foreshortening, and shadowing. While georeferencing methods currently exist for satellite-based SAR, very few have been adapted for UAV platforms. This research seeks to adapt georeferencing algorithms that integrate a digital elevation model (DEM) from a national database and LiDAR measurements. The algorithm that is developed will provide an accurately georeferenced SAR image through geometric corrections. The proposed method aims to create a computationally efficient and more accurate georeferencing algorithm by using the location information recorded by the onboard high accuracy GNSS/INS system that provides the local incidence angle, eliminating the need for ground control points and interpolation. The geolocation output of the proposed method will be evaluated to assess alignment by comparing the geolocated corner reflector positions to their known coordinates and to high resolution LiDAR-derived DEMS over an agriculture testbed at the Purdue Agronomy Center for Research and Education (ACRE) and the Martell Forest. The results from this study should ultimately help implement corrections for complex terrain. A further step for this work would be to continue to use this algorithm to correct for radiometric errors. Keywords: Synthetic Aperture Radar (SAR); Unmanned Aerial Vehicles (UAVs); Georeferencing; Digital Elevation Model (DEM); Geometric Correction

Source:

Purdue University / 2025

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Co-authors:

Emiko A Sano