Abstract
The use of UAVs finds application in a variety of fields, among which are the small scale surveys for environmental protection application. In this frame some experimental tests were carried out at Politecnico di Milano to assess metric accuracies of images acquired by UAVs and derived photogrammetric products. A block of 5 strips and 49 photos was taken by fixed wing system SenseFly, carrying a camera Canon Ixus 220HS on a rural area included in an Italian Park. Images are processed through bundle adjustment, automatic DEM extraction and orthoimages production steps with several software packages, with the aim to evaluate their characteristics, capabilities and weaknesses. The software packages tested were Erdas-LPS, EyeDEA (University of Parma), Agisoft Photoscan, Pix4UAV, PhotoModeler Scanner. For the georeferencing of the block 16 pre-signalized ground control points were surveyed in the area through GPS (NRTK survey). Comparison of results is given in terms of differences among orientation parameters and their accuracies. Moreover, comparisons among different digital surface models are evaluated. Furthermore, exterior orientation parameters, image points and ground points coordinates, obtained by the various software packages, were used as initial values in a comparative adjustment made by scientific in-house software. Paper confirms that computer vision software are faster in computation and, even if their main goal is not to pursue high accuracy in points coordinates determination, they seems to produce results comparable to those obtainable with standard photogrammetric approach. Agisoft Photoscan seems in this case to yield the best results in terms of quality of photogrammetric products.
Similar content being viewed by others
References
Barazzetti L, Remondino F, Scaioni M, Brumana R (2010a) Fully automatic UAV image-based sensor orientation. Int Arch Photogramm Remote Sens Spatial Inf Sci XXXVIII(1):6
Barazzetti L, Scaioni M, Remondino F (2010b) Orientation and 3D modeling from markerless terrestrial images: combining accuracy with automation. Photogramm Rec 25(132):356–381
Bay H, Ess A, Tuylelaars T, Van Gool (2008) Speeded Robust Features (SURF). Comput Vis Image Underst 110:346–359
Brown DC (1971) Close-range camera calibration. Photogramm Eng 37(8):855–866
Colomina I, Blazquez M, Molina P, Parés ME, Wis M (2008) Towards a new paradigm for high-resolution low-cost photogrammetry and remote sensing. IAPRS SIS 37(B1):1201–1206
Eisenbeiss H, Sauerbier M (2011) Investigation of UAV systems and flight modes for photogrammetric applications. Photogramm Rec 26(136):400–421
Fischler M, Bolles R (1981) Random sample consensus: a paradigm for model fitting with application to image analysis and automated cartography. Commun Assoc Comp Mach 24:81–95
Forlani G (1986) Sperimentazione del nuovo programma CALGE dell’ITM. Boll SIFET 2:63–72
Fraser CS (2013) Automatic camera calibration in close range photogrammetry. Photogramm Eng Remote Sens 79:381–388
Gini R, Passoni D, Pinto L, Sona G (2012) Aerial images from an UAV system: 3d modeling and tree species classification in a park area. Int Arch Photogramm Remote Sens Spatial Inf Sci XXXIX-B1:361–366
Grenzdörffer GJ, Engel A, Teichert B (2008) The photogrammetric potential of low-cost UAVs in forestry and agriculture. IAPRS SIS 37(B1):1207–1213
Haarbrink RB, Koers E (2006) Helicopter UAV for photogrammetry and rapid response. In: International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISPRS Workshop of Inter-Commission WG I/V, Autonomous Navigation, Antwerp, Belgium
Hartley R, Zisserman A (2006) Multiple view geometry in computer vision. Cambridge University Press, UK
Lowe DG (2004) Distinctive image feature from scale-invariant key points. Int J Comput Vis 60(2):91–110
Pollefeys M, Koch R, Van Gool L (1999) Self-calibration and metric reconstruction inspite of varying and unknown internal camera parameters. IJCV 32(1):7–25
Re C, Roncella R, Forlani G, Cremonese G, Naletto G (2012) Evaluation of area-based image matching applied to DTM generation with Hirise images. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci I-4:209–214
Remondino F, Del Pizzo S, Kersten T, Troisi S (2012) Low-cost and open-source solutions for automated image orientation—a critical overview. Progress in Culturale Heritage Preservation. Lecture Notes in Computer Science. Vol.7616, pp 40–54
Remondino F, Gruen A, von Schwerin J, Eisenbeiss H, Rizzi A, Giraldi S, Sauerbier M, Richards-Rissetto H (2009) Multi-sensor 3D documentation of the Maya site of Copan. Proceedings of the XXIInd CIPA Symposium
Remondino F, Barazzetti L, Nex F, Scaioni M, Sarazzi D (2011) UAV photogrammetry for mapping and 3D modeling—current status and future perspectives. Int Arch Photogramm Remote Sens Spat Inf Sci XXXVIII-1/C22:25–31. doi:10.5194/isprsarchives-XXXVIII-1-C22-25-2011
Remondino F (2006) Detectors and descriptors for photogrammetric applications. Int Arch Photogramm Remote Sens Spat Inf Sci 36(3):4914
Roncella R, Re C, Forlani G (2011) Performance evaluation of a structure and motion strategy in architecture and cultural heritage. Int Arch Photogramm Remote Sens Spatial Inf Sci XXXVIII-5(W16):285–292
Strecha C, Pylvanainen T, Fua P (2010) Dynamic and scalable large scale image reconstruction. Comput Vis Pattern Recognit (CVPR), IEEE Conference on, pp. 406–413
Acknowledgments
The authors would like to thank Dr Riccardo Roncella for providing the software EyeDEA and Terradat company, owned by Paolo Dosso, who performed the flight with SenseFly System.
The project has been sponsored by Regione Lombardia (Italy)
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by: M. Scaioni
Published in the Special Issue Application of Surveying in Land Management with Guest Editors Dr. Marco Scaioni and Dr. Maurício Roberto Veronez.
Rights and permissions
About this article
Cite this article
Sona, G., Pinto, L., Pagliari, D. et al. Experimental analysis of different software packages for orientation and digital surface modelling from UAV images. Earth Sci Inform 7, 97–107 (2014). https://doi.org/10.1007/s12145-013-0142-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12145-013-0142-2