Manual geo‑rectification to improve the spatial accuracy of ortho‑mosaics based on images from consumer‑grade unmanned aerial vehicles (UAVs)

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Manual geo‑rectification to improve the spatial accuracy of ortho‑mosaics based on images from consumer‑grade unmanned aerial vehicles (UAVs). / Azim, Saiful; Rasmussen, Jesper; Nielsen, Jon; Gislum, René; Laursen, Morten Stigaard; Christensen, Svend.

In: Precision Agriculture, Vol. 20, 19.03.2019, p. 1-12.

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@article{3e49e89b447a40029098de2ea45add60,
title = "Manual geo‑rectification to improve the spatial accuracy of ortho‑mosaics based on images from consumer‑grade unmanned aerial vehicles (UAVs)",
abstract = "The global spatial accuracy of ortho-mosaics based on images from consumer-grade unmanned aerial vehicles (UAVs) is relatively low. The use of ground control points (GCPs) improves the accuracy but it requires RTK-GNSS (global navigation satellite system) measurements in the field. The objective of this study was to evaluate manual geo-rectification after ortho-mosaicking as a cost-effective alternative to GCPs or UAVs equipped with RTK-GNSS. Google Earth images and A-B lines used for tractor auto-steering were used for manual geo-rectification with a free geographic information system (GIS) and remote-sensing (RS) software (QGIS). Two different photogrammetry software (Agisoft Photoscan and Pix4DMapper) were used for ortho-mosaicking. Images were captured in three fields at two altitudes (40 and 80 m) with a multi-rotor camera drone equipped with a GNSS without ground reference (Phantom 4). The results showed that flight altitude and photogrammetry software had no impact on the spatial accuracy and that manual geo-rectification significantly improved the spatial accuracy. Root mean squares (RMSEs) for ortho-mosaics without geo-rectification was in the range of 4 to 28 m and the range was 0.6 to 2.5 m and 0.5 to 1.1 m for geo-rectification based on Google Earth images and A-B-lines, respectively. RTK-GNSS tagged UAV images and the use of GCPs gave average RMSEs of less than 0.1 m. It was concluded that manual geo-rectification offers a feasible alternative to GCPs and UAVs with RTK-GNSS when spatial accuracy of about 1 m is acceptable.",
author = "Saiful Azim and Jesper Rasmussen and Jon Nielsen and Ren{\'e} Gislum and Laursen, {Morten Stigaard} and Svend Christensen",
year = "2019",
month = "3",
day = "19",
doi = "10.1007/s11119-019-09647-9",
language = "English",
volume = "20",
pages = "1--12",
journal = "Precision Agriculture",
issn = "1385-2256",
publisher = "Springer New York LLC",

}

RIS

TY - JOUR

T1 - Manual geo‑rectification to improve the spatial accuracy of ortho‑mosaics based on images from consumer‑grade unmanned aerial vehicles (UAVs)

AU - Azim, Saiful

AU - Rasmussen, Jesper

AU - Nielsen, Jon

AU - Gislum, René

AU - Laursen, Morten Stigaard

AU - Christensen, Svend

PY - 2019/3/19

Y1 - 2019/3/19

N2 - The global spatial accuracy of ortho-mosaics based on images from consumer-grade unmanned aerial vehicles (UAVs) is relatively low. The use of ground control points (GCPs) improves the accuracy but it requires RTK-GNSS (global navigation satellite system) measurements in the field. The objective of this study was to evaluate manual geo-rectification after ortho-mosaicking as a cost-effective alternative to GCPs or UAVs equipped with RTK-GNSS. Google Earth images and A-B lines used for tractor auto-steering were used for manual geo-rectification with a free geographic information system (GIS) and remote-sensing (RS) software (QGIS). Two different photogrammetry software (Agisoft Photoscan and Pix4DMapper) were used for ortho-mosaicking. Images were captured in three fields at two altitudes (40 and 80 m) with a multi-rotor camera drone equipped with a GNSS without ground reference (Phantom 4). The results showed that flight altitude and photogrammetry software had no impact on the spatial accuracy and that manual geo-rectification significantly improved the spatial accuracy. Root mean squares (RMSEs) for ortho-mosaics without geo-rectification was in the range of 4 to 28 m and the range was 0.6 to 2.5 m and 0.5 to 1.1 m for geo-rectification based on Google Earth images and A-B-lines, respectively. RTK-GNSS tagged UAV images and the use of GCPs gave average RMSEs of less than 0.1 m. It was concluded that manual geo-rectification offers a feasible alternative to GCPs and UAVs with RTK-GNSS when spatial accuracy of about 1 m is acceptable.

AB - The global spatial accuracy of ortho-mosaics based on images from consumer-grade unmanned aerial vehicles (UAVs) is relatively low. The use of ground control points (GCPs) improves the accuracy but it requires RTK-GNSS (global navigation satellite system) measurements in the field. The objective of this study was to evaluate manual geo-rectification after ortho-mosaicking as a cost-effective alternative to GCPs or UAVs equipped with RTK-GNSS. Google Earth images and A-B lines used for tractor auto-steering were used for manual geo-rectification with a free geographic information system (GIS) and remote-sensing (RS) software (QGIS). Two different photogrammetry software (Agisoft Photoscan and Pix4DMapper) were used for ortho-mosaicking. Images were captured in three fields at two altitudes (40 and 80 m) with a multi-rotor camera drone equipped with a GNSS without ground reference (Phantom 4). The results showed that flight altitude and photogrammetry software had no impact on the spatial accuracy and that manual geo-rectification significantly improved the spatial accuracy. Root mean squares (RMSEs) for ortho-mosaics without geo-rectification was in the range of 4 to 28 m and the range was 0.6 to 2.5 m and 0.5 to 1.1 m for geo-rectification based on Google Earth images and A-B-lines, respectively. RTK-GNSS tagged UAV images and the use of GCPs gave average RMSEs of less than 0.1 m. It was concluded that manual geo-rectification offers a feasible alternative to GCPs and UAVs with RTK-GNSS when spatial accuracy of about 1 m is acceptable.

UR - https://link.springer.com/article/10.1007/s11119-019-09647-9

U2 - 10.1007/s11119-019-09647-9

DO - 10.1007/s11119-019-09647-9

M3 - Journal article

VL - 20

SP - 1

EP - 12

JO - Precision Agriculture

JF - Precision Agriculture

SN - 1385-2256

ER -