RTK GNSS-Assisted Terrestrial SfM Photogrammetry without GCP: Application to Coastal Morphodynamics Monitoring

Marion Jaud (1) , Stéphane Bertin (2, 1) , Mickael Beauverger (2) , Emmanuel Augereau (3) , Christophe Delacourt (2)

1 IUEM - Institut Universitaire Européen de la Mer
2 LGO - Laboratoire Géosciences Océan
3 LETG - Brest - Littoral, Environnement, Télédétection, Géomatique

The present article describes a new and efficient method of Real Time Kinematic (RTK) Global Navigation Satellite System (GNSS) assisted terrestrial Structure-from-Motion (SfM) photogrammetry without the need for Ground Control Points (GCPs). The system only requires a simple frame that mechanically connects a RTK GNSS antenna to the camera. The system is low cost, easy to transport, and offers high autonomy. Furthermore, not requiring GCPs enables saving time during the in situ acquisition and during data processing. The method is tested for coastal cliff monitoring, using both a Reflex camera and a Smartphone camera. The quality of the reconstructions is assessed by comparison to a synchronous Terrestrial Laser Scanner (TLS) acquisition. The results are highly satisfying with a mean error of 0.3 cm and a standard deviation of 4.7 cm obtained with the Nikon D800 Reflex camera and, respectively, a mean error of 0.2 cm and a standard deviation of 3.8 cm obtained with the Huawei Y5 Smartphone camera. This method will be particularly interesting when simplicity, portability, and autonomy are desirable. In the future, it would be transposable to participatory science programs, while using an open RTK GNSS network.

Mots clés: without GCP; coastal monitoring; cliff; SfM photogrammetry; direct georeferencing

Domaines: Traitement du signal et de l'image [eess.SP]; Interfaces continentales, environnement; Géophysique [physics.geo-ph]; Autre

Marion Jaud, Stéphane Bertin, Mickael Beauverger, Emmanuel Augereau, Christophe Delacourt. RTK GNSS-Assisted Terrestrial SfM Photogrammetry without GCP: Application to Coastal Morphodynamics Monitoring. Remote Sensing, 2020, 12, ⟨10.3390/rs12111889⟩. ⟨hal-02865813⟩