Drone research published in Journal of UAS

Published: December 10th, 2017

Category: News

Adam Benjamin collaborated on a UAS mapping operations paper published in the Journal of Unmanned Aerial Systems entitled: “Assessment of Structure from Motion (SfM) processing parameters on processing time, spatial accuracy, and geometric quality of unmanned aerial system derived mapping products”.

Benjamin, A., O’Brien, D., Barnes, G., Wilkinson, B., and Volkmann, W. (2017). “Assessment of Structure from Motion (SfM) processing parameters on processing time, spatial accuracy, and geometric quality of unmanned aerial system derived mapping products.” Journal of Unmanned Aerial Systems, 3(1), 27. https://uasjournal.org/volume-three/article/assessment-structure-motion-sfm-processing-parameters-processing-time-spatial 

Abstract:

As unmanned aerial system (UAS)-related mapping applications grow in number, a corresponding demand for enhancing field to finish UAS mapping workflows is at the forefront of the geospatial industry. This study investigates the impact processing parameter selection used in UAS multi-view stereo (MVS) photogrammetry processing has on the spatial accuracy and geometric quality of UAS-derived orthophotos and digital elevation models. The goal is to temporally optimize the semi-automated workflow by applying an understanding of the tradeoffs between parameter values and accuracy/quality metrics associated with the derived geospatial datasets. With 48 trials using the UAS-MVS representative software package, PhotoScan, results show that less rigorous structure from motion (SfM) processing parameters, specifically alignment and dense cloud generation parameters, can provide time savings without sacrificing the spatial accuracy of UAS-derived mapping products in low to moderate topographic relief areas. Lower ‘quality’ settings in the dense cloud generation phase led to the most significant time savings. When considering geometric quality in addition to spatial accuracy, reducing the alignment ‘accuracy’ and the number of key points does not impact the spatial accuracy of the resultant geospatial datasets.

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