Skip to main content
Log in

Parameter estimation in 3D affine and similarity transformation: implementation of variance component estimation

  • Original Article
  • Published:
Journal of Geodesy Aims and scope Submit manuscript

Abstract

Three-dimensional (3D) coordinate transformations, generally consisting of origin shifts, axes rotations, scale changes, and skew parameters, are widely used in many geomatics applications. Although in some geodetic applications simplified transformation models are used based on the assumption of small transformation parameters, in other fields of applications such parameters are indeed large. The algorithms of two recent papers on the weighted total least-squares (WTLS) problem are used for the 3D coordinate transformation. The methodology can be applied to the case when the transformation parameters are generally large of which no approximate values of the parameters are required. Direct linearization of the rotation and scale parameters is thus not required. The WTLS formulation is employed to take into consideration errors in both the start and target systems on the estimation of the transformation parameters. Two of the well-known 3D transformation methods, namely affine (12, 9, and 8 parameters) and similarity (7 and 6 parameters) transformations, can be handled using the WTLS theory subject to hard constraints. Because the method can be formulated by the standard least-squares theory with constraints, the covariance matrix of the transformation parameters can directly be provided. The above characteristics of the 3D coordinate transformation are implemented in the presence of different variance components, which are estimated using the least squares variance component estimation. In particular, the estimability of the variance components is investigated. The efficacy of the proposed formulation is verified on two real data sets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Acar M, Özlüdemir MT, Akyilmaz O, Celik RN, Ayan T (2006) Deformation analysis with total least squares. Nat Hazards Earth Syst Sci 6–4:663–669

    Article  Google Scholar 

  • Akyilmaz O (2007) Total least-squares solution of coordinate transformation. Surv Rev 39:68–80

    Article  Google Scholar 

  • Amiri-Simkooei AR (2013) Application of least squares variance component estimation to errors-in-variables models. J Geodesy 87:935–944

    Article  Google Scholar 

  • Amiri-Simkooei AR (2016) Non-negative least-squares variance component estimation with application to GPS time series. J Geodesy 90(5):451–466

    Article  Google Scholar 

  • Amiri-Simkooei AR (2017) Weighted total least squares with singular covariance matrices subject to weighted and hard constraints. J Surv Eng 143(4):04017018

    Article  Google Scholar 

  • Amiri-Simkooei AR, Jazaeri S (2012) Weighted total least squares formulated by standard least squares theory. J Geod Sci 2(2):113–124

    Google Scholar 

  • Amiri-Simkooei AR, Teunissen PJG, Tiberius CCJM (2009) Application of least-squares variance component estimation to GPS observables. J Surv Eng 135(4):149–160

    Article  Google Scholar 

  • Amiri-Simkooei AR, Zangeneh-Nejad F, Asgari J (2016) On the covariance matrix of weighted total least squares estimates. J Surv Eng 142(3):04015014

    Article  Google Scholar 

  • Andrei C-O (2006) 3D affine coordinate transformations. M.Sc. Thesis in Geodesy, No. 3091 TRITA-GIT EX 06-004, School of Architecture and the Built Environment, Royal Institute of Technology (KTH), Stockholm, Sweden

  • Baarda W (1973) S-transformations and criterion matrices. Publications on Geodesy, New Series, Vol 5, No 1. Netherlands Geodetic Commission

  • Bomford G (1971) Geodesy, 3rd edn. Oxford University Press, London 742 pp

    Google Scholar 

  • De Agostino M, Lingua A, Piras M (2012) SOLDEO: a new solution for 3D GIS data recording. FIG Working Week 2012, Knowing to manage the territory, protect the environment, evaluate the cultural heritage, Rome, Italy, 6–10 May 2012

  • Deakin RE (1998) 3D coordinate transformations. Surv Land Inf Syst 58(4):223–234

    Google Scholar 

  • El-Sheimy N (1996) The development of VISAT for GIS applications. Ph.D. Dissertation, UCGE Report No. 20101, Department of Geomatics Engineering, The University of Calgary, Alberta, Canada

  • Fang X (2011) Weighted total least squares solutions for applications in Geodesy. Ph.D. dissertation, Publ. No. 294, Department of Geodesy and Geoinformatics, Leibniz University, Hannover, Germany

  • Fang X (2013) Weighted total least squares: necessary and sufficient conditions, fixed and random parameters. J Geod 87:733–749

    Article  Google Scholar 

  • Fang X (2014a) A structured and constrained total least squares solution with cross-covariances. Stud Geophys Geod 58(1):1–16

    Article  Google Scholar 

  • Fang X (2014b) On non-combinatorial weighted total least squares with inequality constraints. J Geod 88(8):805–816

    Article  Google Scholar 

  • Fang X (2015) Weighted total least-squares with constraints: a universal formula for geodetic symmetrical transformations. J Geod 89(5):459–469

    Article  Google Scholar 

  • Fang X, Wu Y (2016) On the errors-in-variables model with equality and inequality constraints for selected numerical examples. Acta Geod Geophys 51(3):515–525

    Article  Google Scholar 

  • Fang X, Li B, Alkhatib H, Zeng W, Yao Y (2017) Bayesian inference for the errors-in-variables model. Stud Geophys Geod 61(1):35–52

    Article  Google Scholar 

  • Fayad AT (1996) Merging both GPS and terrestrial data in the computations of the geodetic control points. Ph.D. thesis, Department of Public Works, Faculty of Engineering, Ain Shams University, Cairo, Egypt

  • Felus YA, Burtch RC (2009) On symmetrical three-dimensional datum conversion. GPS Solut 13(1):65–74

    Article  Google Scholar 

  • Fraser CS, Yamakawa T (2003) Applicability of the affine model for Ikonos image orientation over mountainous terrain. In: Workshop on HRM from Space, 6–8 October, Hanover

  • Golub G, Van Loan C (1980) An analysis of the total least squares problem. SIAM J Numer Anal 17:883–893

    Article  Google Scholar 

  • Grafarend EW, Awange JL (2003) Nonlinear analysis of the three-dimensional datum transformation [conformal group C7(3)]. J Geod 77:66–76

    Article  Google Scholar 

  • Kanatani K, Matsunaga C (2013) Computing internally constrained motion of 3-D sensor data for motion interpretation. Pattern Recogn 46:1700–1709

    Article  Google Scholar 

  • Koch K-R, Kusche J (2002) Regularization of geopotential determination from satellite data by variance components. J Geod 76(5):259–268

    Article  Google Scholar 

  • Kutoglu HS, Ayan T, Mekik C (2006) Integrating GPS with national networks by collocation method. Appl Math Comput 117:508–514

    Google Scholar 

  • Leick A (2004) GPS satellite surveying, 3rd edn. Wiley, New York 435 pp

    Google Scholar 

  • Li B (2016) Stochastic modeling of triple-frequency BeiDou signals: estimation, assessment and impact analysis. J Geodesy 90(7):593–610

    Article  Google Scholar 

  • Mikhail EM, Bethel JS, McGlone JC (2001) Introduction to modern photogrammetry, 1st edn. Wiley, New York

    Google Scholar 

  • Pandey G, McBride J, Savarese S, Eustice R (2010) Extrinsic calibration of a 3D laser scanner and an omni-directional camera. In: 7th IFAC symposium on intelligent autonomous vehicles

  • Park SU, Chung MJ (2013) 3D world modeling using 3D laser scanner and omni-direction. In: 19th Korea-Japan joint workshop on frontiers of computer vision (FCV2013) Nam-Gu Incheon, South Korea

  • Schaffrin B, Felus Y (2008) On the multivariate total least-squares approach to empirical coordinate transformations. Three algorithms. J. Geod. 82:353–383

    Google Scholar 

  • Teunissen PJG (1985a) The geometry of geodetic inverse linear mapping and non-linear adjustment. Netherlands Geodetic Commission, Publications on Geodesy 8(1), Delft

  • Teunissen PJG (1985b) Generalized inverses, adjustment, the datum problem and S-transformations. In: Grafarend EW, Sanso F (eds) Optimization of geodetic networks. Springer, Berlin, pp 11–55

    Chapter  Google Scholar 

  • Teunissen PJG (1988a) The nonlinear 2D symmetric Helmert transformation: an exact nonlinear least-squares solution. Bull Geod 62:1–15

    Article  Google Scholar 

  • Teunissen PJG (1988b) Towards a least-squares framework for adjusting and testing of both functional and stochastic model. Internal research memo, Geodetic Computing Centre, Delft, A reprint of original 1988 report is also available in 2004, No. 26. http://saegnss1.curtin.edu.au/Publications/2004/Teunissen2004To-wards

  • Teunissen PJG (1990) Nonlinear least-squares. Manus Geod 15(3):137–150

    Google Scholar 

  • Teunissen PJG (2004) Adjustment theory: an introduction. Delft University Press, Delft University of Technology, Series on Mathematical Geodesy and Positioning. http://www.vssd.nl/hlf/a030.htm

  • Teunissen PJG, Amiri-Simkooei AR (2008) Least-squares variance component estimation. J Geod 82(2):65–82

    Article  Google Scholar 

  • Tong X, Jin Y, Li L (2011) An improved weighted total least squares method with applications in linear fitting and coordinate transformation. J Surv Eng 137(4):120–128

    Article  Google Scholar 

  • Tong X, Jin Y, Zhang S, Li L, Liu S (2015) Bias-corrected weighted total least-squares adjustment of condition equations. J Surv Eng 141(2):04014013

    Article  Google Scholar 

  • Vanicek P, Krakiwsky E (1986) Geodesy: the concepts. North-Holland, Amsterdam

    Google Scholar 

  • Xu PL, Liu J (2014) Variance components in errors-in-variables models: estimability, stability and bias analysis. J Geod 88(8):719–734

    Article  Google Scholar 

  • Xu PL, Shen Y, Fukuda Y, Liu Y (2006) Variance component estimation in linear inverse ill-posed models. J Geod 80(2):69–81

    Article  Google Scholar 

  • Xu PL, Liu Y, Shen Y, Fukuda Y (2007) Estimability analysis of variance and covariance components. J Geod 81(9):593–602

    Article  Google Scholar 

  • Xu PL, Liu J, Shi C (2012) Total least squares adjustment in partial errors-in-variables models: algorithm and statistical analysis. J Geod 86(8):661–675

    Article  Google Scholar 

  • Zhang S, Zhang K, Liu P (2016) Total least-squares estimation for 2D affine coordinate transformation with constraints on physical parameters. J Surv Eng 142(3):04016009

    Article  Google Scholar 

  • Zhou Y, Kou X, Li J, Fang X (2016) Comparison of structured and weighted total least-squares adjustment methods for linearly structured errors-in-variables models. J Surv Eng 143(1):04016019

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. R. Amiri-Simkooei.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Amiri-Simkooei, A.R. Parameter estimation in 3D affine and similarity transformation: implementation of variance component estimation. J Geod 92, 1285–1297 (2018). https://doi.org/10.1007/s00190-018-1119-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00190-018-1119-1

Keywords

Navigation