Adaptive TerraSAR-X Image Registration (AIR) Using Spatial Fisher Kernel Framework

  • B. SirishaEmail author
  • Chandra Sekhar Paidimarry
  • A. S. Chandrasekhara Sastry
  • B. Sandhya
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10597)


TerraSAR-X image registration is a forerunner for remote sensing application like target detection, which need accurate spatial transformation between the real time sensed image and the reference off-line image. It is observed that the outcome of registration of two TerraSAR images even when acquired from the same sensor is unpredictable with all the parameters of the feature extraction, matching and transformation algorithm are fixed. Hence we have approached the problem by trying to predict if the given TerraSAR-X images that can be registered without actually registering them. The proposed adaptive image registration (AIR) approach incorporates a classifier into the standard pipeline of feature based image registration. The attributes for the classifier model are derived from fusing the spatial parameters of the feature detector with the descriptor vector in Fisher kernel framework. We have demonstrated that the proposed AIR approach saves the time of feature matching and transformation estimation for SAR images which cannot be registered.


  1. 1.
    Kim, S., Song, W.-J., Kim, S.-H.: Robust ground target detection by SAR and IR sensor fusion using adaboost-based feature selection. In: Sensors (2016)Google Scholar
  2. 2.
    El-Darymli, K., Peter, M., Desmond, C.M.: Target detection in synthetic aperture radar imagery: a state-of-the-art survey. IJRS 7(1), 071598 (2013)Google Scholar
  3. 3.
    Perronnin, F., Sánchez, J., Mensink, T.: Improving the fisher kernel for large-scale image classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-15561-1_11 CrossRefGoogle Scholar
  4. 4.
    Perronnin, F., Dance, C.: Fisher kernels on visual vocabularies for image categorization. In: CVPR, p. 18 (2007)Google Scholar
  5. 5.
    Zitova, B., Flusser, J.: Image registration methods: a surveyIn. Image Vis. Comput. 21(11), 977–1000 (2003)CrossRefGoogle Scholar
  6. 6.
    Zhou, D., Zeng, L., Liang, J., Zhang, K.: Improved method for SAR image registration based on scale invariant feature transform. IET Radar Sonar Navig. 11(4), 4 (2017)Google Scholar
  7. 7.
    Fan, J., Wu, Y., Li, M., Liang, W., Zhang, Q.: SAR image registration using multiscale image patch features with sparse representation. Appl. Earth Observations Remote Sens. 10(4), 1483–1493 (2017)CrossRefGoogle Scholar
  8. 8.
    Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. IJCV 60(1), 63–86 (2004)CrossRefGoogle Scholar
  9. 9.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., et al.: A comparison of affine region detectors. Int. J. Comput. Vis. 65, 43 (2005)CrossRefGoogle Scholar
  10. 10.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)CrossRefGoogle Scholar
  11. 11.
    Arandjelovic, R., Zisserman, A.: Three things everyone should know to im-prove object retrieval. In: CVPR (2012)Google Scholar
  12. 12.
    van Gemert, J.C., Geusebroek, J.-M., Veenman, C.J., Smeulders, A.W.M.: Kernel codebooks for scene categorization. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5304, pp. 696–709. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-88690-7_52 CrossRefGoogle Scholar
  13. 13.
    Bombrun, L., Beaulieu, J.-M.: Fisher distribution for texture modeling of polarimetric SAR data. IEEE Geosci. Remote Sens. Lett. 5(3), 512–516 (2008)CrossRefGoogle Scholar
  14. 14.
    Zhang, Y., Zhu, C., Bres, S., Chen, L.: Encoding local binary descriptors by bag-of-features with hamming distance for visual object categorization. In: Serdyukov, P., Braslavski, P., Kuznetsov, S.O., Kamps, J., Rüger, S., Agichtein, E., Segalovich, I., Yilmaz, E. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 630–641. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-36973-5_53 CrossRefGoogle Scholar
  15. 15.
    Arandjelovic, R., Zisserman, A.: All about VLAD. In: CVPR 2013, pp. 1578–1585 (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.KL UniversityGunturIndia
  2. 2.UCE, Osmania UniversityHyderabadIndia
  3. 3.KL UniversityGunturIndia
  4. 4.MVSR Engineering CollegeHyderabadIndia

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