Comparison of the strip- and block-wise aerial triangulation using different exterior orientation parameters weights

References

• Ballhorn, U., et al., 2009. Derivation of burn scar depths and estimation of carbon emissions with LIDAR in Indonesian peatlands. Proceedings of the National Academy of Sciences, 106 (50), 21213–21218. doi:10.1073/pnas.0906457106
   PubMed Web of Science ®Google Scholar
• Baselga, S., 2007. Global optimization solution of robust estimation. Journal of Surveying Engineering, 133 (3), 123–128. doi:10.1061/(ASCE)0733-9453(2007)133:3(123)
   Web of Science ®Google Scholar
• Bjerhammar, A., 1973. A theory of errors and generalized inverse matrices. Amsterdam, New York: Elsevier Scientific Publishing Co.
   Google Scholar
• Blázquez, M. and Colomina, I., 2012. Relative INS/GNSS aerial control in integrated sensor orientation: models and performance. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 120–133. doi:10.1016/j.isprsjprs.2011.11.003
   Web of Science ®Google Scholar
• Copernicus Europe’s eyes on Earth, 2020. Land monitoring service. Available from: https://land.copernicus.eu/ [Accessed 08 Sep 2020].
   Google Scholar
• Cramer, M., 1999. Direct geocoding: is aerial triangulation obsolete. In: Fritsch and Spiller, eds. Photographic week 1999. Berline, Germany: Heidelberg Wichmann, 59–70.
   Google Scholar
• Cramer, M., 2001. Genauigkeitsuntersuchungen zur GPS-INS-Integration in der Aerophotogrammetrie. Verlag der Bayerischen Akademie der Wissenschaften in Kommission bei der C.H. Beck'schen Verlagsbuchhandlung.
   Google Scholar
• Cramer, M., Stallmann, D., and Haala, N., 2000. Direct georeferencing using GPS/inertial exterior orientations for photogrammetric applications. International Archives of Photogrammetry and Remote Sensing, 33 (B3/1; PART 3), 198–205.
   Google Scholar
• Dell Acqua, F., Gamba, P., and Mainardi, A., 2001. Digital terrain models in dense urban areas. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 34 (3/W4), 195–202.
   Google Scholar
• El-Ashmawy, K.L., 2018. Photogrammetric block adjustment without control points. Geodesy and Cartography, 44 (1), 6–13. doi:10.3846/gac.2018.880
   Google Scholar
• Estornell, J., et al., 2011. Analysis of the factors affecting LiDAR DTM accuracy in a steep shrub area. International Journal of Digital Earth, 4 (6), 521–538. doi:10.1080/17538947.2010.533201
   Web of Science ®Google Scholar
• Gerke, M., 2009. Dense matching in high resolution oblique airborne images. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 38 (3/W4), 77–82.
   Google Scholar
• Gerke, M., 2011. Using horizontal and vertical building structure to constrain indirect sensor orientation. ISPRS Journal of Photogrammetry and Remote Sensing, 66 (3), 307–316. doi:10.1016/j.isprsjprs.2010.11.002
   Web of Science ®Google Scholar
• Gruen, A., 2012. Development and status of image matching in photogrammetry. The Photogrammetric Record, 27 (137), 36–57. doi:10.1111/j.1477-9730.2011.00671.x
   Web of Science ®Google Scholar
• Gruszczyński, W., Matwij, W., and Ćwiąkała, P., 2017. Comparison of low-altitude UAV photogrammetry with terrestrial laser scanning as data-source methods for terrain covered in low vegetation. ISPRS Journal of Photogrammetry and Remote Sensing, 126, 168–179. doi:10.1016/j.isprsjprs.2017.02.015
   Web of Science ®Google Scholar
• Gülch, E., 2012. Photogrammetric measurements in fixed wing UAV imagery. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 39 (B1), 381–386. doi:10.5194/isprsarchives-XXXIX-B1-381-2012
   Google Scholar
• Heipke, C., 1999. Automatic aerial triangulation: results of the OEEPE-ISPRS test and current developments. Wichmann: Photogrammetric week, 177–191.
   Google Scholar
• Heipke, C., Jacobsen, K., and Wegmann, H., 2002. Analysis of the results of the OEEPE test “Integrated Sensor Orientation”. In: OEEPE Integrated Sensor Orientation Test Report and Workshop Proceedings, Hannover, Germany, Editors.
   Google Scholar
• Hodgson, M.E., et al., 2003. An evaluation of LIDAR-and IFSAR-derived digital elevation models in leaf-on conditions with USGS Level 1 and Level 2 DEMs. Remote Sensing of Environment, 84 (2), 295–308. doi:10.1016/S0034-4257(02)00114-1
   Web of Science ®Google Scholar
• Hohle, J., 2008. Photogrammetric measurements in oblique aerial images. Photogrammetrie Fernerkundung Geoinformation, 2008 (1), 7.
   Google Scholar
• Ip, A., El-Sheimy, N., and Mostafa, M., 2007. Performance analysis of integrated sensor orientation. Photogrammetric Engineering and Remote Sensing, 73 (1), 89–97. doi:10.14358/PERS.73.1.89
   Web of Science ®Google Scholar
• Jensen, J.L. and Mathews, A.J., 2016. Assessment of image-based point cloud products to generate a bare earth surface and estimate canopy heights in a woodland ecosystem. Remote Sensing, 8 (1), 50. doi:10.3390/rs8010050
   Web of Science ®Google Scholar
• Jouybari, A., et al., 2019. Methods comparison for attitude determination of a lightweight buoy by raw data of IMU. Measurement, 135, 348–354.
   Web of Science ®Google Scholar
• Jouybari, A., Ardalan, A.A., and Rezvani, M.H., 2017. Experimental comparison between Mahoney and Complementary sensor fusion algorithm for attitude determination by raw sensor data of Xsens IMU on buoy. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 497–502.
   Google Scholar
• Kerner, S., Kaufman, I., and Raizman, Y., 2016. Role of Tie-Points distribution in aerial photography. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 41–44. doi:10.5194/isprsarchives-XL-3-W4-41-2016
   Google Scholar
• Kersten, T., Haering, S., and Ag, S.V., 1998. Automatic tie point extraction using the oeepe/isprs test data—the swissphoto investigations. Hamburg: HafenCity Universität Hamburg. (Report for the pilot center of the oeepe/isprs test, Photogrammetrie & Laserscanning).
   Google Scholar
• Kiraci, A.C. and Toz, G., 2016. Theoretical analysis of positional uncertainty in direct georeferencing. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 41 (p), 1221.
   Google Scholar
• Konecny, K., et al., 2016. Variable carbon losses from recurrent fires in drained tropical peatlands. Global Change Biology, 22 (4), 1469–1480. doi:10.1111/gcb.13186
   PubMed Web of Science ®Google Scholar
• Kraus, K., 2007. Photogrammetry – geometry from images and laser scans. 2nd ed. Vienna, Austria: de Gruyter.
   Google Scholar
• Kruck, E., 2001. Combined IMU and sensor calibration with BINGO-F. In: Integrated Sensor Orientation, Proc. of the OEEPE Workshop, Hannover, Germany, March.
   Google Scholar
• Lantmateriet, 2020. Elevation data - National elevation model. Available from: https://www.lantmateriet.se/sv/Kartor-och-geografisk-information/geodataprodukter/stodsidor/hojddata—nationell-hojdmodell/# [Accessed 14 Sep 2020].
   Google Scholar
• Lembicz, B.W., 2006. Minimizing ground control when gps photogrammetry isn’t practical. In: ASPRS 2006 Annual Conference, Reno, Nevada, May, 1–5.
   Google Scholar
• Madani, M. and Mostafa, M.M.R., 2001. ISAT direct exterior orientation QA/QC strategy using POS data. In: Proceedings of OEEPE Workshop: Integrated Sensor Orientation, September, Hanover, Germany, 17–18.
   Google Scholar
• Madani, M. and Shkolnikov, I., 2005. Dynamic drift model for GPS/INS post-processed trajectory of frame camera. In: ISPRS Hanover Workshop, Germany.
   Google Scholar
• Mesas-Carrascosa, F.J., et al., 2014. Positional quality assessment of orthophotos obtained from sensors onboard multi-rotor UAV platforms. Sensors, 14 (12), 22394–22407. doi:10.3390/s141222394
   PubMed Web of Science ®Google Scholar
• Mostafa, M.M., Hutton, J., and Lithopoulos, E., 2001a. Airborne direct georeferencing of frame imagery: an error budget. In: Proceedings of the 3rd International Symposium on Mobile Mapping Technology (MMS2001), Cairo, Egypt, January.
   Google Scholar
• Mostafa, M.R. and Hutton, J., 2001b. Airborne kinematic positioning and attitude determination without base stations. In: Proceedings, International Symposium on Kinematic Systems in Geodesy, Geomatics, and Navigation (KIS 2001), June, Banff, Alberta, Canada.
   Google Scholar
• Nframes, 2020. Sure aerial - The solution for city and countrywide mapping with aerial imagery. Available from: https://www.nframes.com/products/sure-aerial/ [Accessed 06 Nov 2020].
   Google Scholar
• Osada, E., 2001. Geodesy. Wrocław: Wroclaw University of Science and Technology Publishing. (In Polish).
   Google Scholar
• Pfeifer, N., Glira, P., and Briese, C., 2012. Direct georeferencing with on board navigation components of light weight UAV platforms. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39, 487–492. doi:10.5194/isprsarchives-XXXIX-B7-487-2012
   Google Scholar
• Rahmayudi, A. and Rizaldy, A., 2016. Comparison of semi automatic DTM from image matching with DTM from Lidar. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 41, 373–380.
   Google Scholar
• Reddy, A.D., et al., 2015. Quantifying soil carbon loss and uncertainty from a peatland wildfire using multi-temporal LiDAR. Remote Sensing of Environment, 170, 306–316. doi:10.1016/j.rse.2015.09.017
   Web of Science ®Google Scholar
• Rizaldy, A. and Firdaus, W., 2012. Direct georeferencing: A new standard in photogrammetry for high accuracy mapping. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39, B1.
   Google Scholar
• Salach, A., et al., 2018. Accuracy assessment of point clouds from LidaR and dense image matching acquired using the UAV platform for DTM creation. ISPRS International Journal of Geo-Information, 7 (9), 342. doi:10.3390/ijgi7090342
   Web of Science ®Google Scholar
• Scherzinger, B.M., 1996. Inertial navigator error models for large heading uncertainty. In: Proceedings of Position, Location and Navigation Symposium-PLANS’96. Atlanta, GA, IEEE, April, 477–484.
   Google Scholar
• Schmitz, M., et al., 2001. Benefit of Rigorous Modeling of GPS in Combined AT/GPS/IMU-Bundle Block Adjustment. In: OEEPE Workshop on Integrated Sensor Orientation, Organisation Europene d’Etudes Photogrammtriques Exprimentales/European Organization for Experimental Photogrammetric Research (OEEPE), Hannover, September.
   Google Scholar
• Seifert, E., et al., 2019. Influence of drone altitude, image overlap, and optical sensor resolution on multi-view reconstruction of forest images. Remote Sensing, 11 (10), 1252. doi:10.3390/rs11101252
   Web of Science ®Google Scholar
• Serifoglu, C., Gungor, O., and Yilmaz, V., 2016. Performance evaluation of different ground filtering algorithms for UAV-based point clouds. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 41, 245–251.
   Google Scholar
• Shi, J., et al., 2017. GPS real-time precise point positioning for aerial triangulation. GPS Solutions, 21 (2), 405–414. doi:10.1007/s10291-016-0532-2
   Web of Science ®Google Scholar
• Simpson, J.E., Smith, T.E., and Wooster, M.J., 2017. Assessment of errors caused by forest vegetation structure in airborne LiDAR-derived DTMs. Remote Sensing, 9 (11), 1101. doi:10.3390/rs9111101
   Web of Science ®Google Scholar
• Snavely, N., Seitz, S.M., and Szeliski, R., 2008. Modeling the world from internet photo collections. International Journal of Computer Vision, 80 (2), 189–210. doi:10.1007/s11263-007-0107-3
   Web of Science ®Google Scholar
• Stöcker, C., et al., 2017. Quality assessment of combined IMU/GNSS data for direct georeferencing in the context of UAV-based mapping. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 355.
   Google Scholar
• Terrasolid, 2020. TerraScan – software for LiDAR data processing and 3D vector data creation. Available from: http://www.terrasolid.com/products/terrascanpage.php [Accessed 06 Nov 2020].
   Google Scholar
• Tomaštík, J., et al., 2017. Accuracy of photogrammetric UAV-based point clouds under conditions of partially-open forest canopy. Forests, 8 (5), 151. doi:10.3390/f8050151
   Web of Science ®Google Scholar
• Triggs, B., et al., 1999. Bundle adjustment—a modern synthesis. In: International workshop on vision algorithms, September. Berlin, Heidelberg: Springer, 298–372.
   Google Scholar
• Trimble, 2015. MATCH-AT reference manual. Germany: Trimble inpho.
   Google Scholar
• Truong Giang, N., et al., 2018. Second iteration of photogrammetric processing to refine image orientation with improved tie-points. Sensors, 18 (7), 2150. doi:10.3390/s18072150
   Web of Science ®Google Scholar
• Tsai, V.J., Kao, J.S., and Chen, C.N., 2006. On GPS and GPS-RTK assisted aerotriangulation. In: Proceedings of ASPRS 2006 Annual Conference. Reno, Nevada: ASPRS, 1–10.
   Google Scholar
• Wallace, L., et al., 2016. Assessment of forest structure using two UAV techniques: A comparison of airborne laser scanning and structure from motion (SfM) point clouds. Forests, 7 (3), 62. doi:10.3390/f7030062
   Web of Science ®Google Scholar
• Wierzbicki, D., 2017. Determination of Shift/Bias in digital aerial triangulation of UAV imagery sequences. In: IOP Conference Series: Earth and Environmental Science Vol. 95, No. 3, December. Prague, Czech Republic, IOP Publishing, 032033.
   Google Scholar
• Xie, L., et al., 2016. An asymmetric re-weighting method for the precision combined bundle adjustment of aerial oblique images. ISPRS Journal of Photogrammetry and Remote Sensing, 117, 92–107. doi:10.1016/j.isprsjprs.2016.03.017
   Web of Science ®Google Scholar
• Yastikli, N., Toth, C., and Grejner-Brzezinska, D.A., 2007. In-situ camera and boresight calibration with LiDAR Data. In: Proc. The Fifth International Symposium on Mobile Mapping Technology, MMT, Padua, Italy, Vol. 7.
   Google Scholar
• Yuan, X., et al., 2009. The application of GPS precise point positioning technology in aerial triangulation. ISPRS Journal of Photogrammetry and Remote Sensing, 64 (6), 541–550. doi:10.1016/j.isprsjprs.2009.03.006
   Web of Science ®Google Scholar
• Zach, C., 2014. Robust bundle adjustment revisited. In: European Conference on Computer Vision, September. Cham: Springer, 772–787.
   Google Scholar
• Zhang, Y., Hu, B., and Zhang, J., 2011. Relative orientation based on multi-features. ISPRS Journal of Photogrammetry and Remote Sensing, 66 (5), 700–707. doi:10.1016/j.isprsjprs.2011.06.001
   Web of Science ®Google Scholar