Retrieving Multiaspect Point Clouds From a Multichannel K-Band SAR Drone - Peter Brotzer; Emiliano Casalini; David Small; Alexander Damm; Elias Mendez Dominguez (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing)
Satellite and airborne synthetic aperture radar (SAR) systems are frequently used for topographic mapping. However, their limited scene aspects lead to reduced angular coverage, making them less effective in environments with complex surface structures and tall objects. This limitation can be overcome by drone-based SAR systems, which are becoming increasingly advanced, but their potential for three-dimensional (3-D) imaging remains largely unexplored. In this article, we utilize multiaspect SAR data acquired with a K-band drone system with 700 MHz bandwidth and investigate the potential 3-D point cloud retrievals in high resolution. Through a series of experiments with increasingly complex 3-D structures, we evaluate the accuracy of the derived point clouds. Independent references—based on light detection and ranging (LiDAR) and 3-D construction models—are used to validate our results. Our findings demonstrate that the drone SAR system can produce accurate and complete point clouds, with average Chamfer distances on the order of 1 m compared to reference data, highlighting the significance of multiple aspect acquisitions for 3-D mapping applications.