Scaled totalleastsquaresbased registration for optical remote sensing imagery
 Yong Ge,
 Tianjun Wu,
 Jianghao Wang,
 Jianghong Ma,
 Yunyan Du
 … show all 5 hide
Rent the article at a discount
Rent now* Final gross prices may vary according to local VAT.
Get AccessAbstract
In optical image registration, the reference control points (RCPs) used as explanatory variables in the polynomial regression model are generally assumed to be error free. However, this most frequently used assumption is often invalid in practice because RCPs always contain errors. In this situation, the extensively applied estimator, the ordinary least squares (LS) estimator, is biased and incapable of handling the errors in RCPs. Therefore, it is necessary to develop new feasible methods to address such a problem. This paper discusses the scaled total least squares (STLS) estimator, which is a generalization of the LS estimator in optical remote sensing image registration. The basic principle and the computational method of the STLS estimator and the relationship among the LS, total least squares (TLS) and STLS estimators are presented. Simulation experiments and real remotely sensed image experiments are carried out to compare LS and STLS approaches and systematically analyze the effect of the number and accuracy of RCPs on the performances in registration. The results show that the STLS estimator is more effective in estimating the model parameters than the LS estimator. Using this estimator based on the errorinvariables model, more accurate registration results can be obtained. Furthermore, the STLS estimator has superior overall performance in the estimation and correction of measurement errors in RCPs, which is beneficial to the study of error propagation in remote sensing data. The larger the RCP number and error, the more obvious are these advantages of the presented estimator.
 Arbia, G, Griffith, D, Haining, R (1999) Error propagation modeling in raster GIS: adding and ratioing operations. Cartogr Geogr Inf Sci 26: pp. 297316 CrossRef
 Brown, DG, Goovaerts, P, Burnicki, A, Li, MY (2002) Stochastic simulation of landcover change using geostatistics and generalized additive models. Photogramm Eng Rem S 68: pp. 10511061
 Burnicki, AC, Brown, DG, Goovaerts, P (2007) Simulating error propagation in landcover change analysis: the implications of temporal dependence. Computers Environment and Urb 31: pp. 282302 CrossRef
 Burnicki, AC, Brown, DG, Goovaerts, P (2010) Propagating error in landcoverchange analyses: impact of temporal dependence under increased thematic complexity. Int J Geogr Inf Sci 24: pp. 10431060 CrossRef
 Carmel, Y, Dean, DJ (2004) Performance of a spatiotemporal error model for raster datasets under complex error patterns. Int J Remote Sens 25: pp. 52835296 CrossRef
 Evans FH (1998) Statistical methods in remote sensing. In: Proceedings of the 3rd National Earth Resource Assessment workshop, Brisbane, Australia, 1618, Nov., pp 126
 Faber, NM, Kowalski, BR (1997) Propagation of measurement errors for the validation of predictions obtained by principal component regression and partial least squares. J Chemometrics 11: pp. 181238 CrossRef
 Felus, YA (2004) Application of total least squares for spatial point process. J Surv Eng 130: pp. 126133 CrossRef
 Ge, Y, Leung, Y, Ma, JH, Wang, JF (2006) Modeling for registration of remotely sensed imagery when reference control points contain error. Sci China Ser D 49: pp. 739746 CrossRef
 Gillard JW (2006) An historical overview of linear regression with errors in both variables. Technical Report, Cardiff School of Mathematics, UK, Available at http://www.cardiff.ac.uk/maths/resources/Gillard_Tech_Report.pdf
 Glasbey, CA, Mardia, KV (1998) A review of imagewarping methods. J Appl Stat 25: pp. 155171 CrossRef
 Goodchild, MF, Sun, GQ, Yang, S (1992) Development and test of an error model for categoricaldata. Int J Geogr Inf Syst 6: pp. 87104 CrossRef
 Jensen, JR (1996) Introductory Digital Image Processing: a remote sensing perspective. Prentice Hall, New Jersey
 Kyriakidis, PC, Dungan, JL (2001) A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological model predictions. Environ Ecol Stat 8: pp. 311330 CrossRef
 Lunetta, RS, Congalton, RG, Fenstermaker, LK, Jensen, JR, McGwire, KC, Tinney, LR (1991) Remote sensing and geographic information system data integration: error sources and research issues. Photogramm Eng Rem S 57: pp. 677687
 Markovsky, I (2010) Bibliography on total least squares and related methods. Statistics and Its Interface 3: pp. 329334
 Markovsky, I, Huffel, S (2007) Overview of total least squares methods. J Signal Process 87: pp. 22832302 CrossRef
 Paige, CC, Strakoš, Z (2002a) Scaled total least squares fundamentals. Numer Math 91: pp. 117146 CrossRef
 Paige CC, Strakoš Z (2002b) Unifying least squares, total least squares and data least squares. In: Van Huffel S, Lemmerling P (eds) Total least squares and errorsinvariables modeling, Kluwer Academic Publishers, pp 25–34
 Ramos, JA (2007) Applications of TLS and related methods in the environmental sciences. Comput Stat Data An 52: pp. 12341267 CrossRef
 Rao BD (1997) Unified treatment of LS, TLS and truncated SVD methods using a weighted TLS framework. In: Van Huffel S (ed) Recent advances in total least squares techniques and errorsinvariables modelling, SIAM, pp 11–20
 Richards, JA, Jia, XP (1999) Remote Sensing Digital Image Analysis: an introduction. Springer, Berlin, New York
 Schaffrin, B, Felus, Y (2008) On the multivariate total leastsquares approach to empirical coordinate transformations: three algorithms. J Geodesy 82: pp. 373383 CrossRef
 Kassteele, J, Stein, A (2006) A model for external drift kriging with uncertain covariates applied to air quality measurements and dispersion model output. Environmetrics 17: pp. 309322 CrossRef
 Huffel, S eds. (1997) Recent advances in total least squares techniques and errorsinvariables modeling. SIAM, Philadelphia
 Huffel, S, Cheng, CL, Mastronardi, N, Paige, C, Kukush, A (2007) Total least squares and errorsinvariables modeling. Comput Stat Data An 52: pp. 10761079 CrossRef
 Huffel, S, Lemmerling, P eds. (2002) Total least squares and errorsinvariables modeling: analysis, algorithms and applications. Kluwer Academic Publishers, Dordrecht
 Huffel, S, Vandewalle, J (1989) On the accuracy of total least squares and least squares techniques in the presence of errors on all data. J Automatica 25: pp. 765769 CrossRef
 Huffel, S, Vandewalle, J (1991) The total least squares problem: computational aspects and analysis. SIAM, Philadelphia CrossRef
 Veregin, H (1995) Developing and testing of an error propagation model for GIS overlay operations. Int J Geogr Inf Sci 9: pp. 595619 CrossRef
 Wansbeek, T, Meijer, E (2000) Measurement error and latent variables in econometrics. Elsevier, New York
 Title
 Scaled totalleastsquaresbased registration for optical remote sensing imagery
 Journal

Earth Science Informatics
Volume 5, Issue 34 , pp 137152
 Cover Date
 20121201
 DOI
 10.1007/s1214501201031
 Print ISSN
 18650473
 Online ISSN
 18650481
 Publisher
 SpringerVerlag
 Additional Links
 Topics
 Keywords

 Image registration
 Polynomial regression model
 Errorinvariables model
 Ordinary least squares
 Scaled total least squares
 Singular value decomposition
 Authors

 Yong Ge ^{(1)}
 Tianjun Wu ^{(2)}
 Jianghao Wang ^{(1)} ^{(3)}
 Jianghong Ma ^{(2)}
 Yunyan Du ^{(1)}
 Author Affiliations

 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
 2. Department of Mathematics and Information Science, Chang’an University, Xi’an, 710064, China
 3. Graduate University of Chinese Academy of Sciences, Beijing, China, 100049