Integrated Matching and Geocoding of SAR and Optical Satellite Images

  • Olaf Hellwich
  • Cornelius Wefelscheid
  • Jakub Lukaszewicz
  • Ronny Hänsch
  • M. Adnan Siddique
  • Adam Stanski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7887)

Abstract

In this paper matching of SAR and optical remote sensing images in object space is investigated. First, the matching performance of descriptors and localizers for point features extracted from preprocessed images is analyzed. In particular, the increase in matching performance when making use of approximate localization information is documented. Secondly, the combined geocoding of SAR and optical image data using both orbit information and digital surface models as well as matched points is introduced and investigated using a radar-photogrammetric least-squares adjustment approach.

Keywords

multimodal image matching SAR optical remote sensing geocoding 

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References

  1. 1.
    Wefelscheid, C., Hellwich, O.: OpenOF: Framework for Sparse Non-Linear Least Squares Optimization on a GPU. In: VISAPP, 8 p. (2013)Google Scholar
  2. 2.
    Fonseca, L.M.G., Manjunath, B.S.: Registration techniques for multisensor remotely sensed imagery. PE&RS 62, 1049–1056 (1996)Google Scholar
  3. 3.
    Alberga, V., Idrissa, M., Lacroix, V., Inglada, J.: Performance Estimation of Similarity Measures of Multi-Sensor Images for Change Detection Applications. MultiTemp, 1–5 (2007)Google Scholar
  4. 4.
    Dare, P., Dowman, I.: An improved model for automatic feature-based registration of SAR and SPOT images. ISPRS Journal 56, 13–28 (2001)CrossRefGoogle Scholar
  5. 5.
    Ali, M.A., Clausi, D.A.: Automatic registration of SAR and visible band remote sensing images. In: IGARSS, vol. 3, pp. 1331–1333 (2002)Google Scholar
  6. 6.
    Inglada, J., Vadon, H.: Fine registration of SPOT5 and Envisat/ASAR images and ortho-image production: a fully automatic approach. In: IGARSS, vol. 5, pp. 3510–3512 (2005)Google Scholar
  7. 7.
    Schwickerath, A.N.A., Beveridge, J.R.: Coregistration of range and optical images using coplanarity and orientation constraints. In: CVPR, pp. 899–906 (1996)Google Scholar
  8. 8.
    Raggam, J., Almer, A., Strobl, D.: A combination of SAR and optical line scanner imagery for stereoscopic extraction of 3-D data. ISPRS Journal 49, 11–21 (1994)CrossRefGoogle Scholar
  9. 9.
    Ebner, H., Fritsch, D., Gillessen, W., Heipke, C.: Integration von Bildzuordnung und Objektrekonstruktion innerhalb der digitalen Photogrammetrie. BuL 55, 194–203 (1987)Google Scholar
  10. 10.
    Werner, C., Strozzi, T., Wegmuller, U., Wiesmann, A.: SAR geocoding and multi-sensor image registration. In: IGARSS, vol. 2, pp. 902–904 (2002)Google Scholar
  11. 11.
    Bradsky, G.: OpenCV (Open Source Computer Vision), http://opencv.willowgarage.com
  12. 12.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: ICCV, vol. 2, pp. 1150–1157 (1999)Google Scholar
  13. 13.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). CVIU 110, 346–359 (2008)Google Scholar
  14. 14.
    Shi, J., Tomasi, C.: Good features to track. In: CVPR, pp. 593–600 (1994)Google Scholar
  15. 15.
    Rothacher, M., Tapley, B.D., Reigber, C., Koenig, R., Falck, C., Grunwaldt, L., Koehler, W., Massmann, F.H., Michalak, G.: The tracking, occultation and ranging (TOR) instrument onboard TerraSAR-X and on TanDEM-X. In: IGARSS, pp. 4983–4986 (2007)Google Scholar
  16. 16.
    Hellwich, O., Ebner, H.: Geocoding SAR Interferograms by Least Squares Adjustment. ISPRS Journal 55, 277–288 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Olaf Hellwich
    • 1
  • Cornelius Wefelscheid
    • 1
  • Jakub Lukaszewicz
    • 1
  • Ronny Hänsch
    • 1
  • M. Adnan Siddique
    • 1
  • Adam Stanski
    • 1
  1. 1.Computer Vision & Remote SensingTechnische Universität BerlinBerlinGermany

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