Skip to main content
Log in

Geometric correction of satellite stereo images by DEM matching without ground control points and map projection step: tested on Cartosat-1 images

  • Research Article
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

Ground Control Points (GCPs) are needed in most geometric processing of satellite images. Generally used geometric model is based on the use of rational polynomials. The Coefficients of Rational Polynomials (RPCs) are provided by image vendors which are contaminated by some biases. In this paper, a no-GCP bias correction method for RPCs of satellite stereo images is introduced. The method uses global DEMs as ground control information: First, a point cloud is generated from stereo pair, then using the DEM matching strategy it is aligned to the global DEM for estimation of 3D rigid transformation parameters. This transformation is performed with our originally developed method which separates three planimetric parameters from three leveling parameters. They are then employed for bias correction in object space. For DEM matching and also for bias correction, we developed new formulae given directly in geodetic longitude and latitude format, instead of Cartesian map projection coordinates. Numerical results of this research are reported in two categories: with or without (1) GCPs and (2) map projection step. Experiments on two Cartosat-1 stereo pairs show in category (1), improvement in geopositioning accuracy from 399.2 m and 124.0 m to 7.6 m and 2.6 m, respectively. In category (2), we observed RMS improvement in both datasets and in all components up to 3.1 m in longitude, 6.8 m in latitude and 1.2 m in height.

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Afsharnia H, Arefi H (2019) A quality assessment on the DEM matching-based RPC bias correction. Earth Observation Geomatics Eng 3(2):12–23

    Google Scholar 

  • Alidoost F, Azizi A, Arefi H (2015) The rational polynomial coefficients modification using digital elevation models. Int Arch Photogramm Remote Sens Spat Inf Sci 40(1):47

    Article  Google Scholar 

  • Alizadeh Naeini A, Fatemi SB, Babadi M, Mirzadeh SMJ, Homayouni S (2020) Application of 30-meter global digital elevation models for compensating rational polynomial coefficients biases. Geocarto Int 35(12):1311–1326

  • Bagheri H, Schmitt M, Zhu XX (2018) Fusion of TanDEM-X and Cartosat-1 elevation data supported by neural network-predicted weight maps. ISPRS J Photogramm Remote Sens 144:285–297

    Article  Google Scholar 

  • Bhang KJ, Schwartz FW, Braun A (2007) Verification of the vertical error in C-band SRTM DEM using ICESat and Landsat-7, otter Tail County, MN. IEEE Trans Geosci Remote Sens 45(1):36–44

    Article  Google Scholar 

  • Chen X, Zhang B, Cen M, Guo H, Zhang T, Zhao C (2017) SRTM DEM-aided mapping satellite-1 image geopositioning without ground control points. IEEE Geosci Remote Sens Lett 14(11):2137–2141

    Article  Google Scholar 

  • d’Angelo P, Reinartz P (2012) DSM based orientation of large stereo satellite image blocks. Int Arch Photogramm Remote Sens Spat Inf Sci 39(B1):209–214

    Article  Google Scholar 

  • d'Angelo P, Uttenthaler A, Carl S, Barner F, Reinartz P (2010) Automatic generation of high quality DSM based on IRS-P5 Cartosat-1 stereo data, vol SP-686. Special Publication, pp 1–5

    Google Scholar 

  • Ebner H, Müller F (1986) Processing of digital three-line imagery using a generalized model for combined point determination. Photogrammetria 41(3):173–182

    Article  Google Scholar 

  • Fraser CS, Dial G, Grodecki J (2006) Sensor orientation via RPCs. ISPRS J Photogramm Remote Sens 60(3):182–194

    Article  Google Scholar 

  • Grodecki J (2001 IKONOS stereo feature extraction-RPC approach. ASPRS Annual Conference St. Louis, USA, 23–27 April 2001 (Bethesda, MD: ASPRS), CD-ROM (unpaginated)

  • Gruen A, Akca D (2005) Least squares 3D surface and curve matching. ISPRS J Photogramm Remote Sens 59(3):151–174

    Article  Google Scholar 

  • Hasegawa H, Matsuo K, Koarai M, Watanabe N, Masaharu H, Fukushima Y (2000) DEM accuracy and the base to height (B/H) ratio of stereo images. Int Arch Photogramm Remote Sens 33(B4/1; PART 4):356–359

    Google Scholar 

  • JPL., NASA (2013) "NASA Shuttle Radar Topography Mission Global 1 arc second. https://lpdaac.usgs.gov/products/srtmgl1v003/ [Data set]." In, edited by NASA LP DAAC

  • Karras GE, Petsa E (1993) DEM matching and detection of deformation in close-range photogrammetry without control. Photogramm Eng Remote Sens 59(9):1419–1424

    Google Scholar 

  • Kim T, Jeong J (2010) Precise mapping of high resolution satellite images without ground control points. Int Arch Photogramm Remote Sens Spat Inf Sci 38(5) CD-ROM (unpaginated)

  • Kim T, Jeong J (2011) DEM matching for bias compensation of rigorous pushbroom sensor models. ISPRS J Photogramm Remote Sens 66(5):692–699

    Article  Google Scholar 

  • Klein H, Förstner W (1984) Realization of automatic error detection in the block adjustment program PAT-M43 using robust estimators. Int Arch Photogramm Remote Sens 25(A3a):234–245

    Google Scholar 

  • Lee H, Park B-W, Ahn K (2017) Accuracy improvement of KOMPSAT-3 DEM using previous DEMs without ground control points. J Korean Soc Surv Geod Photogramm Cartogr 35(4):241–248

    Google Scholar 

  • Li R, Di K, Ma R (2003) 3-D shoreline extraction from IKONOS satellite imagery. Mar Geod 26(1–2):107–115

    Article  Google Scholar 

  • Li R, Niu X, Liu C, Wu B, Deshpande S (2009) Impact of imaging geometry on 3D geopositioning accuracy of stereo IKONOS imagery. Photogramm Eng Remote Sens 75(9):1119–1125

    Article  Google Scholar 

  • Li G, Tang X, Gao X, Wang H, Yu W (2016) ZY-3 block adjustment supported by glas laser altimetry data. Photogramm Rec 31(153):88–107

    Article  Google Scholar 

  • Marsetič A, Oštir K, Fras MK (2015) Automatic orthorectification of high-resolution optical satellite images using vector roads. IEEE Trans Geosci Remote Sens 53(11):6035–6047

    Article  Google Scholar 

  • Misra I, Manthira Moorthi S, Dhar D, Ramakrishnan R (2015) A unified software framework for automatic precise Georeferencing of large remote sensing image archives. Procedia Comput Sci 46:812–819

    Article  Google Scholar 

  • Noh M-J, Howat IM (2018) Automatic relative RPC image model bias compensation through hierarchical image matching for improving DEM quality. ISPRS J Photogramm Remote Sens 136:120–133

    Article  Google Scholar 

  • Oh J, Lee C, Seo DC (2013) Automated HRSI georegistration using orthoimage and SRTM: focusing KOMPSAT-2 imagery. Comput Geosci 52:77–84

    Article  Google Scholar 

  • Pan H, Tao C, Zou Z (2016) Precise georeferencing using the rigorous sensor model and rational function model for ZiYuan-3 strip scenes with minimum control. ISPRS J Photogramm Remote Sens 119:259–266

    Article  Google Scholar 

  • Phan VH, Lindenbergh R, Menenti M (2012) ICESat derived elevation changes of Tibetan lakes between 2003 and 2009. Int J Appl Earth Obs Geoinf 17:12–22

    Google Scholar 

  • Rastogi G, Agrawal R, Ajai (2015) Bias corrections of CartoDEM using ICESat-GLAS data in hilly regions. GISci Remote Sens 52(5):571–585

    Article  Google Scholar 

  • Rodríguez E, Morris CS, Belz JE, Chapin EC, Martin JM, Daffer W, Hensley S (2005) An assessment of the SRTM topographic products. In.: technical report JPL D-31639, jet Propulsion Laboratory, Pasadena, California

  • Rosenholm D, Torlegard K (1987) Three-dimensional absolute orientation of stereo models using digital elevation models. Photogramm Eng Remote Sens 54:1385–1389

    Google Scholar 

  • Safdarinezhad A, Mokhtarzade M, Zoej MJV (2016) Coregistration of satellite images and airborne lidar data through the automatic bias reduction of rpcs. IEEE J Sel Top Appl Earth Obs Remote Sens 10(2):749–762

    Article  Google Scholar 

  • Schenk T, Csatho B (2012) A new methodology for detecting ice sheet surface elevation changes from laser altimetry data. IEEE Trans Geosci Remote Sens 50(9):3302–3316

    Article  Google Scholar 

  • Snyder JP (1982) Map projections used by the US geological survey. US Government Printing Office

    Google Scholar 

  • Streutker DR, Glenn NF, Shrestha R (2011) A slope-based method for matching elevation surfaces. Photogramm Eng Remote Sens 77(7):743–750

    Article  Google Scholar 

  • Tao CV, Hu Y (2001) A comprehensive study of the rational function model for photogrammetric processing. Photogramm Eng Remote Sens 67(12):1347–1358

    Google Scholar 

  • Teo T-A, Huang S-H (2013) Automatic co-registration of optical satellite images and airborne LiDAR data using relative and absolute orientations. IEEE J Sel Top Appl Earth Obs Remote Sens 6(5):2229–2237

    Article  Google Scholar 

  • Toutin T (2004) Geometric processing of remote sensing images: models, algorithms and methods. Int J Remote Sens 25(10):1893–1924

    Article  Google Scholar 

  • Toutin T, Schmitt CV, Wang H (2012) Impact of no GCP on elevation extraction from WorldView stereo data. ISPRS J Photogramm Remote Sens 72:73–79

    Article  Google Scholar 

  • Wang J, Di K, Li R (2005) Evaluation and improvement of geopositioning accuracy of IKONOS stereo imagery. J Surv Eng 131(2):35–42

    Article  Google Scholar 

  • Wu B, Tang S, Zhu Q, Tong K-y, Hu H, Li G (2015) Geometric integration of high-resolution satellite imagery and airborne LiDAR data for improved geopositioning accuracy in metropolitan areas. ISPRS J Photogramm Remote Sens 109:139–151

    Article  Google Scholar 

  • Yue L, Shen H, Zhang L, Zheng X, Zhang F, Yuan Q (2017) High-quality seamless DEM generation blending SRTM-1, ASTER GDEM v2 and ICESat/GLAS observations. ISPRS J Photogramm Remote Sens 123:20–34

    Article  Google Scholar 

  • Zwally HJ, Schutz R, Bentley C, Bufton J, Herring T, Minster J, Spinhirne J, Thomas R (2014) GLAS/ICESat L2 global land surface altimetry data, version 34. In: Boulder, Colorado USA. NASA DAAC at the National Snow and Ice Data Center Distributed Active Archive Center

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hamed Afsharnia.

Additional information

Communicated by: H. Babaie

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix 1

Substitution of Eq. (6) in Eq. (4) and neglecting the last term corresponding to scale parameter m results in:

$$ {h}_a-h=-\left(\frac{\partial {h}_a}{\partial \lambda}\frac{\partial \lambda }{\partial X}\right)R\updelta \lambda \cos \varphi -\left(\frac{\partial {h}_a}{\partial \varphi}\frac{\partial \varphi }{\partial Y}\right)R\updelta \varphi +\Delta h+\left(R\left(\varphi -{\varphi}_c\right)+\left(\frac{\partial {h}_a}{\partial \varphi}\frac{\partial \varphi }{\partial Y}\right)h\right)\varOmega +\left(-R\left(\lambda -{\lambda}_c\right)\cos \varphi -\left(\frac{\partial {h}_a}{\partial \lambda}\frac{\partial \lambda }{\partial X}\right)h\right)\varPhi +\left(\left(\frac{\partial {h}_a}{\partial \lambda}\frac{\partial \lambda }{\partial X}\right)R\left(\varphi -{\varphi}_c\right)-\left(\frac{\partial {h}_a}{\partial \varphi}\frac{\partial \varphi }{\partial Y}\right)R\left(\lambda -{\lambda}_c\right)\cos \varphi \right)K $$
(A.1)

Noting that

$$ \frac{\partial \lambda }{\partial X}=\frac{1}{R\cos \varphi },\kern0.5em \frac{\partial \varphi }{\partial Y}=\frac{1}{R} $$
(A.2)

and denoting \( \frac{\partial {h}_a}{\partial \lambda }={S}_{\lambda } \) and \( \frac{\partial {h}_a}{\partial \varphi }={S}_{\varphi } \) we obtain:

$$ {h}_a-h=-{S}_{\lambda}\left(\frac{1}{R\cos \varphi}\right)R\updelta \lambda \cos \varphi -{S}_{\varphi}\left(\frac{1}{R}\right)R\updelta \varphi +\Delta h+\left(R\left(\varphi -{\varphi}_c\right)+{S}_{\varphi}\left(\frac{1}{R}\right)h\right)\varOmega +\left(-R\left(\lambda -{\lambda}_c\right)\cos \varphi -{S}_{\lambda}\left(\frac{1}{R\cos \varphi}\right)h\right)\varPhi +\left({S}_{\lambda}\left(\frac{1}{R\cos \varphi}\right)R\left(\varphi -{\varphi}_c\right)-{S}_{\varphi}\left(\frac{1}{R}\right)R\left(\lambda -{\lambda}_c\right)\cos \varphi \right)K $$
(A.3)

Two terms \( {S}_{\varphi}\left(\frac{1}{R}\right) h\varOmega \) and \( {S}_{\lambda}\left(\frac{1}{R\cos \varphi}\right) h\varPhi \) are negligible because 1/R is a small real number (R ≈ 6371000m) and its multiplication by Ω and Φ which are themselves already small numbers becomes nearly zero. After some simplifications we obtain:

$$ {h}_a-h=-{S}_{\lambda}\updelta \lambda -{S}_{\varphi}\updelta \varphi +\Delta h+\left(\varphi -{\varphi}_c\right) R\varOmega -\left(\lambda -{\lambda}_c\right)\cos \varphi R\varPhi +\left({S}_{\lambda}\frac{\varphi -{\varphi}_c}{\cos \varphi }-{S}_{\varphi}\left(\lambda -{\lambda}_c\right)\cos \varphi \right)K $$
(A.4)

Denoting Q1 =  and Q2 =  we obtain the same equation as Eq. (8).

Appendix 2

Updating the horizontal location of IDEM after jth (passing from jth to (j + 1)th) iteration using Eq. (12) is done by:

$$ \left[\begin{array}{c}{\lambda}^{\left(j+1\right)}-{\lambda}_c\\ {}{\varphi}^{\left(j+1\right)}-{\varphi}_c\end{array}\right]=\left[\begin{array}{cc}1& \frac{\kappa^{(j)}}{\cos \varphi}\\ {}-{\kappa}^{(j)}\cos \varphi & 1\end{array}\right]\left[\begin{array}{c}{\lambda}^{(j)}-{\lambda}_c\\ {}{\varphi}^{(j)}-{\varphi}_c\end{array}\right]+\left[\begin{array}{c}\delta {\lambda}^{(j)}\\ {}\delta {\varphi}^{(j)}\end{array}\right] $$
(B.1)

Passing from jth to (j + 2)th iteration gives:

$$ \left[\begin{array}{c}{\lambda}^{\left(j+2\right)}-{\lambda}_c\\ {}{\varphi}^{\left(j+2\right)}-{\varphi}_c\end{array}\right]=\left[\begin{array}{cc}1& \frac{\kappa^{\left(j+1\right)}}{\cos \varphi}\\ {}-{\kappa}^{\left(j+1\right)}\cos \varphi & 1\end{array}\right]\left(\left[\begin{array}{cc}1& \frac{\kappa^{(j)}}{\cos \varphi}\\ {}-{\kappa}^{(j)}\cos \varphi & 1\end{array}\right]\left[\begin{array}{c}{\lambda}^{(j)}-{\lambda}_c\\ {}{\varphi}^{(j)}-{\varphi}_c\end{array}\right]+\left[\begin{array}{c}\delta {\lambda}^{(j)}\\ {}\delta {\varphi}^{(j)}\end{array}\right]\right)+\left[\begin{array}{c}\delta {\lambda}^{\left(j+1\right)}\\ {}\delta {\varphi}^{\left(j+1\right)}\end{array}\right] $$
(B.2)

In the same way, passing from jth to (j + 3)th iteration gives:

$$ \left[\begin{array}{c}{\lambda}^{\left(j+3\right)}-{\lambda}_c\\ {}{\varphi}^{\left(j+3\right)}-{\varphi}_c\end{array}\right]=\left[\begin{array}{cc}1& \frac{\kappa^{\left(j+2\right)}}{\cos \varphi}\\ {}-{\kappa}^{\left(j+2\right)}\cos \varphi & 1\end{array}\right]\left\{\left[\begin{array}{cc}1& \frac{\kappa^{\left(j+1\right)}}{\cos \varphi}\\ {}-{\kappa}^{\left(j+1\right)}\cos \varphi & 1\end{array}\right]\left(\left[\begin{array}{cc}1& \frac{\kappa^{(j)}}{\cos \varphi}\\ {}-{\kappa}^{(j)}\cos \varphi & 1\end{array}\right]\left[\begin{array}{c}{\lambda}^{(j)}-{\lambda}_c\\ {}{\varphi}^{(j)}-{\varphi}_c\end{array}\right]+\left[\begin{array}{c}\delta {\lambda}^{(j)}\\ {}\delta {\varphi}^{(j)}\end{array}\right]\right)+\left[\begin{array}{c}\delta {\lambda}^{\left(j+1\right)}\\ {}\delta {\varphi}^{\left(j+1\right)}\end{array}\right]\right\}+\left[\begin{array}{c}\delta {\lambda}^{\left(j+2\right)}\\ {}\delta {\varphi}^{\left(j+2\right)}\end{array}\right] $$
(B.3)

After multiplications and neglection of two multiplied azimuthal rotation angles κ, we obtain:

$$ {\lambda}^u-{\lambda}_c=\lambda -{\lambda}_c+\frac{\varphi -{\varphi}_c}{\cos \varphi }{\sum}_{j=1}^J{\kappa}^{(j)}+{\sum}_{j=1}^J\delta {\lambda}^{(j)}+\frac{1}{\cos \varphi}\left({\sum}_{j=1}^{J-1}\delta {\varphi}^{(j)}\right)\left({\sum}_{i=1}^{J-j}{\kappa}^{\left(i+j\right)}\right) $$
(B.4)
$$ {\varphi}^u-{\varphi}_c=\varphi -{\varphi}_c-\left(\lambda -{\lambda}_c\right)\cos \varphi {\sum}_{j=1}^J{\kappa}^{(j)}+{\sum}_{j=1}^J\delta {\varphi}^{(j)}-\cos \varphi \left({\sum}_{j=1}^{J-1}\delta {\lambda}^{(j)}\right)\left({\sum}_{i=1}^{J-j}{\kappa}^{\left(i+j\right)}\right) $$
(B.5)

where (λu, φu) are geodetic coordinates after the last iteration. Regarding to Eq. (B.4) and Eq. (B.5), the last terms in both equations are considerably small and one can neglect them. However, in Eq. (B.4) we pay attention that it is not valid for near-polar regions where cosφc goes to zero. Substitution from Eq. (13) to Eq. (B.4) and Eq. (B.5) gives:

$$ {\lambda}^u-{\lambda}_c=\lambda -{\lambda}_c+\frac{\varphi -{\varphi}_c}{\cos \varphi }K+\Delta \lambda $$
(B.6)
$$ {\varphi}^u-{\varphi}_c=\varphi -{\varphi}_c-\left(\lambda -{\lambda}_c\right)\cos \varphi K+\Delta \varphi $$
(B.7)

Taking into account the leveling parameters, estimated from Eq. (14), and combining with Eq. (B.6) and Eq. (B.7) the final matrix form of proposed BCM as Eq. (16) is obtained.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Afsharnia, H., Arefi, H. & Abbasi, M. Geometric correction of satellite stereo images by DEM matching without ground control points and map projection step: tested on Cartosat-1 images. Earth Sci Inform 15, 1183–1199 (2022). https://doi.org/10.1007/s12145-022-00799-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-022-00799-3

Keywords

Navigation