Abstract
In this paper, the authors describe a Python customized code based on Agisoft MetaShape processing engine that permits the automatic solution of a complete photogrammetric process from acquisition of the image block by Unmanned Aerial Vehicle (UAV) to final results: Dense Digital Surface Model (DDSM), Digital Terrain Model (DTM) and orthophoto.
Inspired by the old approach on analytical stereo-plotter, the proposed solution is based on a partition of the aerial block in a series of strips that can be transmitted by drones to the processing units during the flight to obtain a “quasi-real-time” solution in a just few minutes at the end of the flight.
The Python code can automatically add images from remote folders creating new Metashape Chunks at the end of each strip; align images of each strip in few seconds using the approximate external parameters of images acquired by drone navigation sensors; recognize coded (and not) markers (GCPs) and make a bundle block solution of each strip; align different chunks in a unique photogrammetric block; solve the final photogrammetric block using camera pose optimization of Metashape with an automatic selection of CPs from the recognized markers; compile and show a report that permits the resulting diagnostic by a skilled user.
The proposed solution has been applied to a precision agriculture environment for automatically surveying a vineyard and recognize the rows and the ground areas for automatic path planning purposes.
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Acknowledgements
The study was carried out within the activities of the PoliTO Interdepartmental Centre for Service Robotics (PIC4SeR) and thanks to the concession of the “Azienda Agricola Ciabot” farm as regards the possibility of operating in the case study area.
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Appendix: Python Source Code
Appendix: Python Source Code
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a)
New chunk creation, images alignment and marker detection
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b)
Dense cloud generation
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c)
3D model creation
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DDSM and DTM generation (including cloud classification)
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e)
Orthophoto production
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f)
Binary map production
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Aicardi, I., Angeli, S., Milazzo, R., Lingua, A.M., Musci, M.A. (2020). A Python Customization of Metashape for Quasi Real-Time Photogrammetry in Precision Agriculture Application. In: Parente, C., Troisi, S., Vettore, A. (eds) R3 in Geomatics: Research, Results and Review. R3GEO 2019. Communications in Computer and Information Science, vol 1246. Springer, Cham. https://doi.org/10.1007/978-3-030-62800-0_18
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