SIFT-FANN: An efficient framework for spatio-spectral fusion of satellite images

  • Kunal Kumar Rai
  • Aparna Rai
  • Kanishka Dhar
  • J. Senthilnath
  • S. N. Omkar
  • Ramesh K.N
Research Article


Image fusion techniques are widely used for remote sensing data. A special application is for using low resolution multi-spectral image with high resolution panchromatic image to obtain an image having both spectral and spatial information. Alignment of images to be fused is a step prior to image fusion. This is achieved by registering the images. This paper proposes the methods involving Fast Approximate Nearest Neighbor (FANN) for automatic registration of satellite image (reference image) prior to fusion of low spatial resolution multi-spectral QuickBird satellite image (sensed image) with high spatial resolution panchromatic QuickBird satellite image. In the registration steps, Scale Invariant Feature Transform (SIFT) is used to extract key points from both images. The keypoints are then matched using the automatic tuning algorithm, namely, FANN. This algorithm automatically selects the most appropriate indexing algorithm for the dataset. The indexed features are then matched using approximate nearest neighbor. Further, Random Sample Consensus (RanSAC) is used for further filtering to obtain only the inliers and co-register the images. The images are then fused using Intensity Hue Saturation (IHS) transform based technique to obtain a high spatial resolution multi-spectral image. The results show that the quality of fused images obtained using this algorithm is computationally efficient.


Image fusion Scale invariant feature transform Fast approximate nearest Neighbor 



The authors would like to thank Dr. H. Honne Gowda from KSTA, Bangalore and P.G. Diwakar from ISRO, Bangalore, India for providing the satellite images.

The authors would like to thank the anonymous reviewers for their comments.


  1. Al-Wassai, F. A., Kalyankar, N. V., & Al-Zuky, A. A. (2011). The IHS Transformations Based Image Fusion. Global Research in Computer Science, 2(5), 70–77.Google Scholar
  2. Bay, H., Ess, A., Tuytelaars, T., & Gool, L. J. V. (2008). SURF: Speeded Up Robust Features. Computer Vision and Image Understanding (CVIU), 110(3), 346–359.CrossRefGoogle Scholar
  3. Brown, L. G. (1992). A survey of image registration techniques. ACM Computing Surveys (CSUR) Archive, 24(4), 325–376.CrossRefGoogle Scholar
  4. Deza, Elena; Deza, Michel Marie (2009). Encyclopedia of distances. Springer. p. 94.Google Scholar
  5. Dong, J., Zhuang, D., Huang, Y., & Jingying, F. (2009). Advances in Multi-Sensor Data Fusion: Algorithms and Applications. Sensors, 9(10), 7771–7784.CrossRefGoogle Scholar
  6. Fischer M. A., Bolles R.C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM. Google Scholar
  7. González-Audícana, M., Saleta, J. L., Catalán, O. G., & García, R. (2004). Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition. IEEE Transactions on Geoscience and Remote Sensing, 42(6), 1291–1299.CrossRefGoogle Scholar
  8. Heather, J. P., & Smith, M. I. (2005). Multimodel image registration with applications to image fusion. In 8th international conference on information fusion. Philadelphia: USA.Google Scholar
  9. Kour, G., & Singh, S. P. (2013). Image Fusion Parameter estimation and comparison between SVD and DWT technique. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2(11).Google Scholar
  10. Kumar, U., Mukhopadhyay, C., & Ramachandra, T. V. (2009). Fusion of multisensor data: review and comparative analysis. Intelligent Systems, GCIS '09. WRI Global Congress, 2, 418–422.CrossRefGoogle Scholar
  11. Kumar, A. N., Miga, M. I., Pheiffer, T. S., Chambless, L. B., Thompson, R. C., & Dawant, B. M. (2015). Persistent and automatic intraoperative 3D digitization of surfaces under dynamic magnifications of an operating microscope. Medical Image Analysis, 19(1), 30–45.CrossRefGoogle Scholar
  12. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.CrossRefGoogle Scholar
  13. Muja M., Lowe D. G. (2009). Fast approximate nearest neighbors with automatic algorithm configuration. Proceedings of the International Conference on Computer Vision Theory and Applications. Google Scholar
  14. Padwick C., Deskevich M., Pacifici F., Smallwood S. (2010). Worldview-2 pan-sharpening. Proc ASPRS.Google Scholar
  15. Senthilnath, J., & Prasad, R. (2014). A new sift matching criteria in a genetic algorithm framework for registering multisensory satellite imagery. In Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing ACM (21).Google Scholar
  16. Senthilnath, J., Kalro, N. P., & Benediktsson, J. A. (2014a). accurate point matching based on multi-objective genetic algorithm for multi-sensor satellite imagery. Applied Mathematics and Computation, 236, 546–564.CrossRefGoogle Scholar
  17. Senthilnath, J., Yang, X. S., & Benediktsson, J. A. (2014b). Automatic registration of multi-temporal remote sensing images based on nature-inspired techniques. International Journal of Image and Data Fusion, 5(4), 263–284.Google Scholar
  18. Silpa-Anan C. and Hartley R. (2008). Optimized KD-trees for fast image descriptor matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Google Scholar
  19. Thomas J., Bowyer K. W., Kareem A. (2011). Towards a robust automated hurricane damage assessment from high resolution images. 13th International Conference on Wind Engineering. Google Scholar
  20. Wang, Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error measurement to structural similarity. lEEE Transaction on Image Processing, 13(4), 600–612.CrossRefGoogle Scholar
  21. Zitova, B., & Flusser, J. (2003). Image registration methods: a survey.Image and vision computing, 21(11), 977–1000.Google Scholar

Copyright information

© Indian Society of Remote Sensing 2016

Authors and Affiliations

  • Kunal Kumar Rai
    • 1
  • Aparna Rai
    • 1
  • Kanishka Dhar
    • 2
  • J. Senthilnath
    • 3
  • S. N. Omkar
    • 3
  • Ramesh K.N
    • 4
  1. 1.Department of Electronics and Communication EngineeringNIT SrinagarSrinagarIndia
  2. 2.Department of Computer Science EngineeringNIT SrinagarSrinagarIndia
  3. 3.Department of Aerospace EngineeringIndian Institute of ScienceBengaluruIndia
  4. 4.Department of Electronics and Communication EngineeringUVCE BangaloreBangaloreIndia

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