Quality assessment of image matchers for DSM generation -- a comparative study based on UAV images
Rongjun Qin, Armin Gruen, Cive Fraser
TL;DR
This study compares five commercial and public software tools for dense image matching in UAV imagery to evaluate their accuracy in generating digital surface models, using LiDAR and manual data as benchmarks.
Contribution
It provides a comparative analysis of different image matching software for UAV-based DSM generation, highlighting their relative performance and accuracy.
Findings
APS and MICMAC showed highest accuracy
SURE and Pix4UAV performed well in complex terrains
SGM from DLR provided competitive results
Abstract
Recently developed automatic dense image matching algorithms are now being implemented for DSM/DTM production, with their pixel-level surface generation capability offering the prospect of partially alleviating the need for manual and semi-automatic stereoscopic measurements. In this paper, five commercial/public software packages for 3D surface generation are evaluated, using 5cm GSD imagery recorded from a UAV. Generated surface models are assessed against point clouds generated from mobile LiDAR and manual stereoscopic measurements. The software packages considered are APS, MICMAC, SURE, Pix4UAV and an SGM implementation from DLR.
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