Fusion of Optical and SAR Images

  • Florence Tupin
Part of the Remote Sensing and Digital Image Processing book series (RDIP, volume 15)


There are nowadays many kinds of remote sensing sensors: optical sensors (by this we essentially mean the panchromatic sensors), multi-spectral sensors, hyper-spectral sensors, SAR (Synthetic Aperture Radar) sensors, LIDAR, etc. They have all their own specifications and are adapted to different applications, like land-use, urban planning, ground movement monitoring, Digital Elevation Model computation, etc. But why using jointly SAR and optical sensors? There are two main reasons: first, they hopefully provide complementary information; secondly, SAR data only may be available in some crisis situations, but previously acquired optical data may help their interpretation.


Optical Image Optical Data Markov Random Field Height Field Likelihood Term 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors are indebted to ONERA Office National d’Etudes et de Recherches Arospatiales and to DGA Dlgation Gnrale pour l’Armement for providing the data. They also thank CNES for providing data and financial support in the framework of the scientific proposal R-S06/OT04-010.


  1. Aschbacher J, Pongsrihadulchai A, Karnchanasutham S, Rodprom C, Paudyal D, Toan TL (1996) ERS SAR data for rice crop mapping and monitoring. Second ERS application workshop, London, UK, pp 21–24Google Scholar
  2. Bendjebbour A, Delignon Y, Fouque L, Samson V, Pieczynski W (2002) Multisensor image segmentation using Dempster–Shafer fusion in Markov fields context. IEEE Trans Geo Remote Sens 40(10):2291–2299CrossRefGoogle Scholar
  3. Besag J (1986) On the statistical analysis of dirty pictures. J R Statist Soc B 48(3):259–302Google Scholar
  4. Briem G, Benediktsson J, Sveinsson J (2002) Multiple classifiers applied to multisource remote sensing data. IEEE Trans Geosci Remote Sens 40(10):2291–2299CrossRefGoogle Scholar
  5. Brown R et al (1996) Complementary use of ERS-SAR and optical data for land cover mapping in Johor, Malaysia, year = 1996, Second ERS application workshop, London, UK, pp 31–35Google Scholar
  6. Calabresi G (1996) The use of ERS data for flood monitoring: an overall assessment. Second ERS application workshop, London, UK, pp 237–241Google Scholar
  7. Camps-Valls G, Gomez-Chova L, Munoz-Mari J, Rojo-Alvarez J, Martinez-Ramon M, Serpico M, and Roli F (2008) Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection. IEEE Trans Geosci Remote Sens 46(6):1822–1835CrossRefGoogle Scholar
  8. Chen C, Ho P (2008) Statistical pattern recognition in remote sensing. Pattern Recogn 41(9):2731–2741CrossRefGoogle Scholar
  9. Cloude SR, Pottier E (1997) An entropy based classification scheme for land applications of polarimetric SAR. IEEE Trans Geosci Remote Sens 35(1):68–78CrossRefGoogle Scholar
  10. Dare P, Dowman I (2000) Automatic registration of SAR and SPOT imagery based on multiple feature extraction and matching, IGARSS’00, pp 24–28Google Scholar
  11. Denis L, Tupin F, Darbon J, Sigelle M (2009a) SAR image regularization with fast approximate discrete minimization. IEEE Trans. on Image Processing 18(7):1588–1600. http://www., 2009Google Scholar
  12. Denis L, Tupin F, Darbon J, Sigelle M (2009b) Joint regularization of phase and amplitude of InSAR data: application to 3D reconstruction,Geoscience and Remote Sensing, 47(11):3774-3785,, 2009Google Scholar
  13. Geman S, Geman D (1984) Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE Trans Pattern Anal Machine Intel PAMI-6(6):721–741CrossRefGoogle Scholar
  14. Goodman J (1976) Some fundamental properties of speckle. J Opt Soc Am 66(11):1145–1150CrossRefGoogle Scholar
  15. Harris C, Stephens M (1988) A combined corner and edge detector. In: Proceedings of the 4th Alvey vision conference, Manchester, pp 147–151Google Scholar
  16. Hegarat-Mascle SL, Bloch I, Vidal-Madjar D (2002a) Application of Dempster–Shafer evidence theory to unsupervised classification in multisource remote sensing. IEEE Trans Geosci Remote Sens 35(4):1018–1030CrossRefGoogle Scholar
  17. Hegarat-Mascle SL, Bloch I, Vidal-Madjar D (2002b) Introduction of neighborhood information in evidence theory and application to data fusion of radar and optical images with partial cloud cover. Pattern Recogn 40(10):1811–1823Google Scholar
  18. Hill M, Ticehurst C, Lee J-S, Grunes M, Donald G, Henry D (2005) Integration of optical and radar classifications for mapping pasture type in Western Australia. IEEE Trans Geosci Remote Sens 43:1665–1681CrossRefGoogle Scholar
  19. Hong TD, Schowengerdt RA (2005) A robust technique for precise registration of radar and optical satellite images, Photogram Eng Remote Sens 71(5):585–594Google Scholar
  20. Inglada J, Adragna F (2001) Automatic multi-sensor image registration by edge matching using genetic algorithms, IGARSS’01, pp 113–116Google Scholar
  21. Inglada J, Giros A (2004) On the possibility of automatic multisensor image registration. IEEE Trans Geosci Remote Sens 42(10):2104–2120CrossRefGoogle Scholar
  22. Junjie Z, Chibiao D, Hongjian Y, Minghong X (2006) 3D reconstruction of buildings based on high-resolution SAR and optical images, IGARSS’06Google Scholar
  23. Lehureau G, Tupin F, Tison C, Oller G, Petit D (2008) Registration of metric resolution SAR and optical images in urban areas. In: EUSAR 08, june 2008Google Scholar
  24. Lombardo P, Oliver C, Pellizeri T, Meloni M (2003) A new maximum-likelihood joint segmentation technique for multitemporal SAR and multiband optical images. IEEE Trans Geosci Remote Sens 41(11):2500–2518CrossRefGoogle Scholar
  25. Maitre H, Luo W (1992) Using models to improve stereo reconstruction. IEEE Trans Pattern Anal Machine Intel, pp. 269–277Google Scholar
  26. Modestino JW, Zhang J (1992) A Markov random field model-based approach to image interpretation. IEEE Trans Pattern Anal Machine Intel 14(6):606–615CrossRefGoogle Scholar
  27. Moigne JL, Morisette J, Cole-Rhodes A, Netanyahu N, Eastman R, Stone H (2003) Earth science imagery registration, IGARSS’03, pp 161–163Google Scholar
  28. Pacifici F, Frate FD, Emery W, Gamba P, Chanussot J (2008) Urban mapping using coarse SAR and optical data: outcome of the 2007 GRSS data fusion contest. IEEE Geosci Remote Sens Lett 5:331–335CrossRefGoogle Scholar
  29. Reddy BS, Chatterji BN (1996) A FFT-based technique for translation, rotation and scale-invariant image registration. IEEE Trans Image Proces 5(8):12661271Google Scholar
  30. Rellier G, Descombes X, Zerubia J (2000) Deformation of a cartographic road network on a SPOT satellite image. Int Conf Image Proces 2:736–739Google Scholar
  31. Serpico S, Roli F (1995) Classification of multisensor remote-sensing images by structured neural networks. IEEE Trans Geosci Remote Sens 33(3):562–578CrossRefGoogle Scholar
  32. Shabou A, Tupin F, Chaabane F (2007) Similarity measures between SAR and optical images, IGARSS’07, 4858–4861, 2007Google Scholar
  33. Soergel U, Cadario E, Thiele A, Thoennessen U (2008) Building recognition from multi-aspect high-resolution InSAR data in urban areas. IEEE J Selected Topics Appl Earth Observ Remote Sens 1(2):147–153CrossRefGoogle Scholar
  34. Solberg A, Taxt T, Jain A (1996) A Markov random field model for classification of multisource satellite imagery. IEEE Trans Geosci Remote Sens 34(1):100 – 113CrossRefGoogle Scholar
  35. Tison C, Nicolas J, Tupin F, Maître H (2004) A new statistical model of urban areas in high-resolution SAR images for Markovian segmentation. IEEE Trans Geosci Remote Sens 42(10):2046–2057CrossRefGoogle Scholar
  36. Toutin T, Gray L (2000) State of the art of elevation extraction from satellite SAR data. ISPRS J Photogram Remote Sens 55:13–33CrossRefGoogle Scholar
  37. Tupin F, Roux M (2003) Detection of building outlines based on the fusion of SAR and optical features. ISPRS J Photogram Remote Sens 58(1-2):71–82CrossRefGoogle Scholar
  38. Tupin F, Roux M (2004) 3D information extraction by structural matching of SAR and optical features. In: ISPRS’2004, Istanbul, Turquey, 2004Google Scholar
  39. Tupin F, Roux M (2005) Markov random field on region adjacency graphs for the fusion of SAR and optical data in radargrammetric applications. IEEE Trans Geosci Remote Sens 43(8):1920–1928CrossRefGoogle Scholar
  40. Tupin F, Maître H, Mangin J-F, Nicolas J-M, Pechersky E (1998) Detection of linear features in SAR images: application to road network extraction. IEEE Trans Geosci Remote Sens 36(2):434–453CrossRefGoogle Scholar
  41. Wang Y, Tang M, Tan T, Tai X (2004) Detection of circular oil tanks based on the fusion of SAR and optical images, Third international conference on image and graphics, Hong Kong, ChinaGoogle Scholar
  42. Wang X, Wang G, Guan Y, Chen Q, Gao L (2005) Small satellite constellation for disaster monitoring in China, IGARSS’05, 2005Google Scholar
  43. Waske B, Benediktsson J (2008) Fusion of support vector machines for classification of multisensor data. IEEE Trans Geosci Remote Sens 45(12):3858–3866CrossRefGoogle Scholar
  44. Waske B, der Linden SV (2008) Classifying multilevel imagery from SAR and optical sensors by decision fusion. IEEE Trans Geosci Remote Sens 46(5):1457 – 1466CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Institut TELECOM, TELECOM ParisTech, CNRS LTCIParisFrance

Personalised recommendations