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Automating Ground Control Point Detection in Drone Imagery: From Computer Vision to Deep Learning

Authors :
Gonzalo Muradás Odriozola
Klaas Pauly
Samuel Oswald
Dries Raymaekers
Source :
Remote Sensing, Vol 16, Iss 5, p 794 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Drone-based photogrammetry typically requires the task of georeferencing aerial images by detecting the center of Ground Control Points (GCPs) placed in the field. Since this is a very labor-intensive task, it could benefit greatly from automation. In this study, we explore the extent to which traditional computer vision approaches can be generalized to deal with variability in real-world drone data sets and focus on training different residual neural networks (ResNet) to improve generalization. The models were trained to detect single keypoints of fixed-sized image tiles with a historic collection of drone-based Red–Green–Blue (RGB) images with black and white GCP markers in which the center was manually labeled by experienced photogrammetry operators. Different depths of ResNets and various hyperparameters (learning rate, batch size) were tested. The best results reached sub-pixel accuracy with a mean absolute error of 0.586. The paper demonstrates that this approach to drone-based mapping is a promising and effective way to reduce the human workload required for georeferencing aerial images.

Details

Language :
English
ISSN :
20724292
Volume :
16
Issue :
5
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.fc0e4933e0b645a29f860a06b253c53b
Document Type :
article
Full Text :
https://doi.org/10.3390/rs16050794