Point clouds repeatability and fast scale factor estimates in free SfM surveying: terrestrial application and empirical approach
Language
English
Obiettivo Specifico
OST5 Verso un nuovo Monitoraggio
Status
Published
JCR Journal
JCR Journal
Peer review journal
Yes
Journal
Issue/vol(year)
5/66 (2023)
ISSN
2037-416X
Publisher
INGV
Pages (printed)
RS529
Date Issued
2023
Abstract
Previous experiments highlighted the possible existence of a relation between repeatability of point
clouds obtained from Structure-from-Motion photogrammetry (SfM), represented by the standard
deviation (๐), and the nominal ground sampling distance (GSD). In particular, the empirical relation
3๐ โผ 2.5 GSD was found. For this reason, in-situ tests aimed at studying this relation were carried
out. Data from seven surveys carried out in 2018-2022 time span allowed the comparison between 20
pairs of almost contemporary point clouds, generated by means of relative bundle adjustment (BA)
without ground control points (GCPs) and then relatively scaled and aligned. In this way, the
relation 3๐ = aGSD was found, where a = 2.5 ยฑ 0.4. This result also suggested the use of the reverse
procedure, where the scale factor (SF) is estimated from the standard deviation of non-metric point
clouds, ๐nmu, by using the relation SFa = aGSD/3๐nmu. Additional checks proved that SFa differs
from SF by 3%. This error is not acceptable error for length, area or volume measurements, but
the estimated SFa is more than adequate for a fast, rough registration of photogrammetric models
aimed at searching patterns or precursors of incipient phenomena in impervious/inaccessible areas
or in emergency conditions.
clouds obtained from Structure-from-Motion photogrammetry (SfM), represented by the standard
deviation (๐), and the nominal ground sampling distance (GSD). In particular, the empirical relation
3๐ โผ 2.5 GSD was found. For this reason, in-situ tests aimed at studying this relation were carried
out. Data from seven surveys carried out in 2018-2022 time span allowed the comparison between 20
pairs of almost contemporary point clouds, generated by means of relative bundle adjustment (BA)
without ground control points (GCPs) and then relatively scaled and aligned. In this way, the
relation 3๐ = aGSD was found, where a = 2.5 ยฑ 0.4. This result also suggested the use of the reverse
procedure, where the scale factor (SF) is estimated from the standard deviation of non-metric point
clouds, ๐nmu, by using the relation SFa = aGSD/3๐nmu. Additional checks proved that SFa differs
from SF by 3%. This error is not acceptable error for length, area or volume measurements, but
the estimated SFa is more than adequate for a fast, rough registration of photogrammetric models
aimed at searching patterns or precursors of incipient phenomena in impervious/inaccessible areas
or in emergency conditions.
References
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Motion photogrammetry for high-resolution coastal and fluvial geomorphic surveys, Gรฉomorphologie,
22, 2, 147-161. https://doi.org/10.1007/978-3-319-58304-4_9.
Cutugno, M., U. Robustelli and G. Pugliano (2022). Structure-from-Motion 3D Reconstruction of the Historical
Overpass Ponte della Cerra: A Comparison between MicMacยฎ Open Source Software and Metashapeยฎ, Drones,
6, 9, 242, https://doi.org/10.3390/drones6090242.
Eltner, A, A. Kaiser, C. Castillo, G. Rock, F. Neugirg and A. Abellรกn (2016). Image-based surface reconstruction in
geomorphometry-merits, limits and developments, Earth Surf. Dyn., 4, 2, 359-389, https://doi.org/10.5194/
esurf-4-359-2016.
Ente di gestione per i Parchi e la Biodiversitร (2023). Contrafforte Pliocenico Nature Reserve web page. Available
online at: https://enteparchi.bo.it/en/contrafforte-pliocenico-nature-reserve/ (accessed: November 23, 2023).
Martรญnez-Carricondo, P., F. Agรผera-Vega, F. Carvajal-Ramรญrez, F.โJ. Mesas-Carrascosa, A. Garcรญa-Ferrer and
F.โJ. PรฉrezโPorras (2018). Assessment of UAV-photogrammetric mapping accuracy based on variation of
ground control points, Int. J. Appl. Earth Obs. Geoinf., 72, 1-10, https://doi.org/10.1016/j.jag.2018.05.015.
Mistretta, F., G. Sanna, F. Stochino and G. Vacca (2019). Structure from Motion Point Clouds for Structural Monitoring,
Remote Sens., 11, 1940, https://doi.org/10.3390/rs11161940.
Pesci, A., G. Teza, M. Bisson, F. Muccini, P. Stefanelli, M. Anzidei, R. Carluccio, I. Nicolosi, A. Galvani, V. Sepe and
C. Carmisciano (2016). A fast method for monitoring the coast through independent photogrammetric
measurements: application and case study, J. Geosci. Geomat., 4, 4, 73-81. https://doi.og/10.12691/jgg-4-4-1.
Pesci, A., V. Kastelic, G. Teza, M. Carafa, P. Burrato and R. Basili (2018). Utilizzo della fotogrammetria SfM terrestre
per il monitoraggio dei versanti: considerazioni sulle precisioni per applicazioni a lunga distanza, Rapporto
tecnico INGV, 394, 1-20.
Pesci, A., G. Teza and F. Loddo (2019). Low cost Structure-from-Motion-based fast surveying of a rock cliff: precision
and reliability assessment, Quad. Geofis., INGV, 156, 1-22. https://doi.org/10.13127/qdg/156.
Pesci, A., G. Teza, V. Kastelic and M.M.C. Carafa (2020). Resolution and precision of fast, long range terrestrial
photogrammetric surveying aimed at detecting slope changes, J. Surv. Eng., 146, 4, 04020017-1-13. https://
doi.org/10.1061/(ASCE)SU.1943-5428.0000328.
Pesci, A., G. Teza, F. Loddo, M. Fabris, M. Monego and S. Amoroso (2022). Studio di possibili effetti sistematici nelle
nuvole di punti SfM da APR: confronti con TLS, distorsioni e metodi di mitigazione/Evaluation of possible
systematic effects in SfM UAV based point clouds: TLS and surface variations for error mitigation methods,
Quad. Geofis., INGV, 177, 1-22, https://doi.org/10.13127/qdg/177.
Salas Lรณpez, R., R.E. Terrones Murga, J.O. Silva-Lรณpez, N.B. Rojas-Briceรฑo, D. Gรณmez Fernรกndez, M. Oliva-Cruz and
Y. Taddia (2022). Accuracy Assessment of Direct Georeferencing for Photogrammetric Applications Based on
UAS-GNSS for High Andean Urban Environments, Drones, 6, 12, 388, https://doi.org/10.3390/drones6120388.
Sanz-Ablanedo, E., J.H. Chandler, J.R. Rodrรญguez-Pรฉrez and C. Ordรณรฑez (2018). Accuracy of Unmanned Aerial
Vehicle (UAV) and SfM Photogrammetry Survey as a Function of the Number and Location of Ground Control
Points Used, Remote Sens., 10, 10, 1606, https://doi.org/10.3390/rs10101606.
Yan, L., R. Chen, H. Sun, Y. Sun, L. Liu and Q. Wang (2017). A novel bundle adjustment method with additional ground
control point constraint, Remote Sen. Lett., 8, 1, 68-77, https://doi.org/10.1080/2150704X.2016.1235809.
Zhang, H., E. Aldana-Jague, F. Clapuyt, F. Wilken, V. Vanacker and K. Van Oost (2019). Evaluating the potential of
post-processing kinematic (PPK) georeferencing for UAV-based structure- from-motion (SfM) photogrammetry
and surface change detection, Earth Surf. Dyn., 7, 807-827, https://doi.org/10.5194/esurf-7-807-2019.
Motion photogrammetry for high-resolution coastal and fluvial geomorphic surveys, Gรฉomorphologie,
22, 2, 147-161. https://doi.org/10.1007/978-3-319-58304-4_9.
Cutugno, M., U. Robustelli and G. Pugliano (2022). Structure-from-Motion 3D Reconstruction of the Historical
Overpass Ponte della Cerra: A Comparison between MicMacยฎ Open Source Software and Metashapeยฎ, Drones,
6, 9, 242, https://doi.org/10.3390/drones6090242.
Eltner, A, A. Kaiser, C. Castillo, G. Rock, F. Neugirg and A. Abellรกn (2016). Image-based surface reconstruction in
geomorphometry-merits, limits and developments, Earth Surf. Dyn., 4, 2, 359-389, https://doi.org/10.5194/
esurf-4-359-2016.
Ente di gestione per i Parchi e la Biodiversitร (2023). Contrafforte Pliocenico Nature Reserve web page. Available
online at: https://enteparchi.bo.it/en/contrafforte-pliocenico-nature-reserve/ (accessed: November 23, 2023).
Martรญnez-Carricondo, P., F. Agรผera-Vega, F. Carvajal-Ramรญrez, F.โJ. Mesas-Carrascosa, A. Garcรญa-Ferrer and
F.โJ. PรฉrezโPorras (2018). Assessment of UAV-photogrammetric mapping accuracy based on variation of
ground control points, Int. J. Appl. Earth Obs. Geoinf., 72, 1-10, https://doi.org/10.1016/j.jag.2018.05.015.
Mistretta, F., G. Sanna, F. Stochino and G. Vacca (2019). Structure from Motion Point Clouds for Structural Monitoring,
Remote Sens., 11, 1940, https://doi.org/10.3390/rs11161940.
Pesci, A., G. Teza, M. Bisson, F. Muccini, P. Stefanelli, M. Anzidei, R. Carluccio, I. Nicolosi, A. Galvani, V. Sepe and
C. Carmisciano (2016). A fast method for monitoring the coast through independent photogrammetric
measurements: application and case study, J. Geosci. Geomat., 4, 4, 73-81. https://doi.og/10.12691/jgg-4-4-1.
Pesci, A., V. Kastelic, G. Teza, M. Carafa, P. Burrato and R. Basili (2018). Utilizzo della fotogrammetria SfM terrestre
per il monitoraggio dei versanti: considerazioni sulle precisioni per applicazioni a lunga distanza, Rapporto
tecnico INGV, 394, 1-20.
Pesci, A., G. Teza and F. Loddo (2019). Low cost Structure-from-Motion-based fast surveying of a rock cliff: precision
and reliability assessment, Quad. Geofis., INGV, 156, 1-22. https://doi.org/10.13127/qdg/156.
Pesci, A., G. Teza, V. Kastelic and M.M.C. Carafa (2020). Resolution and precision of fast, long range terrestrial
photogrammetric surveying aimed at detecting slope changes, J. Surv. Eng., 146, 4, 04020017-1-13. https://
doi.org/10.1061/(ASCE)SU.1943-5428.0000328.
Pesci, A., G. Teza, F. Loddo, M. Fabris, M. Monego and S. Amoroso (2022). Studio di possibili effetti sistematici nelle
nuvole di punti SfM da APR: confronti con TLS, distorsioni e metodi di mitigazione/Evaluation of possible
systematic effects in SfM UAV based point clouds: TLS and surface variations for error mitigation methods,
Quad. Geofis., INGV, 177, 1-22, https://doi.org/10.13127/qdg/177.
Salas Lรณpez, R., R.E. Terrones Murga, J.O. Silva-Lรณpez, N.B. Rojas-Briceรฑo, D. Gรณmez Fernรกndez, M. Oliva-Cruz and
Y. Taddia (2022). Accuracy Assessment of Direct Georeferencing for Photogrammetric Applications Based on
UAS-GNSS for High Andean Urban Environments, Drones, 6, 12, 388, https://doi.org/10.3390/drones6120388.
Sanz-Ablanedo, E., J.H. Chandler, J.R. Rodrรญguez-Pรฉrez and C. Ordรณรฑez (2018). Accuracy of Unmanned Aerial
Vehicle (UAV) and SfM Photogrammetry Survey as a Function of the Number and Location of Ground Control
Points Used, Remote Sens., 10, 10, 1606, https://doi.org/10.3390/rs10101606.
Yan, L., R. Chen, H. Sun, Y. Sun, L. Liu and Q. Wang (2017). A novel bundle adjustment method with additional ground
control point constraint, Remote Sen. Lett., 8, 1, 68-77, https://doi.org/10.1080/2150704X.2016.1235809.
Zhang, H., E. Aldana-Jague, F. Clapuyt, F. Wilken, V. Vanacker and K. Van Oost (2019). Evaluating the potential of
post-processing kinematic (PPK) georeferencing for UAV-based structure- from-motion (SfM) photogrammetry
and surface change detection, Earth Surf. Dyn., 7, 807-827, https://doi.org/10.5194/esurf-7-807-2019.
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