The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Unmanned aerial systems for photogrammetry and remote sensing: A review
Open accessAbstract
We discuss the evolution and state-of-the-art of the use of Unmanned Aerial Systems (UAS) in the field of Photogrammetry and Remote Sensing (PaRS). UAS, Remotely-Piloted Aerial Systems, Unmanned Aerial Vehicles or simply, drones are a hot topic comprising a diverse array of aspects including technology, privacy rights, safety and regulations, and even war and peace. Modern photogrammetry and remote sensing identified the potential of UAS-sourced imagery more than thirty years ago. In the last five years, these two sister disciplines have developed technology and methods that challenge the current aeronautical regulatory framework and their own traditional acquisition and processing methods. Navety and ingenuity have combined off-the-shelf, low-cost equipment with sophisticated computer vision, robotics and geomatic engineering. The results are cm-level resolution and accuracy products that can be generated even with cameras costing a few-hundred euros. In this review article, following a brief historic background and regulatory status analysis, we review the recent unmanned aircraft, sensing, navigation, orientation and general data processing developments for UAS photogrammetry and remote sensing with emphasis on the nano-micro-mini UAS segment.
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Citations
Cites
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A Survey on LiDAR Scanning Mechanisms
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Advances in intelligent and autonomous navigation systems for small UAS
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Object Detection and Tracking with UAV Data Using Deep Learning
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“Both satellite imagery and low altitude Unmanned Aerial Vehicle (UAV) embedded sensors are used, but the latter are preferred for their finer-grained resolution and high-throughput pheno-typing abilities (Colomina and Molina 2014, Haghighattalab et al. 2016).”
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Metadata
- Authors
- Colomina, I., & Molina, P.
- Venue
- ISPRS Journal of Photogrammetry and Remote Sensing
- Publisher
- Elsevier BV
- Published
- 2014-06
- Volume
- 92
- Pages
- 79-97
- ISSN
- 0924-2716 (ISSN-L)
- OpenAlex
- W1998943389
- Cited by
- 2,954
- References
- 5
- FWCI
- 272.86
- Type
- journal article
- Language
- en
- Open access
- hybrid
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