Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
권호 표지
DOI QR코드

DOI QR Code

Fusion Techniques Comparison of GeoEye-1 Imagery

  • Kim, Yong-Hyun (Satellite Data application Department, Satellite Information Research Institute, Korea Aerospace Research Institute) ;
  • Kim, Yong-Il (Department of Civil and Environmental Engineering, Seoul National University) ;
  • Kim, Youn-Soo (Satellite Data application Department, Satellite Information Research Institute, Korea Aerospace Research Institute)
  • Published : 2009.12.30
Download PDF
⟨ Previous Next ⟩

Abstract

Many satellite image fusion techniques have been developed in order to produce a high resolution multispectral (MS) image by combining a high resolution panchromatic (PAN) image and a low resolution MS image. Heretofore, most high resolution image fusion techniques have used IKONOS and QuickBird images. Recently, GeoEye-1, offering the highest resolution of any commercial imaging system, was launched. In this study, we have experimented with GeoEye-1 images in order to evaluate which fusion algorithms are suitable for these images. This paper presents compares and evaluates the efficiency of five image fusion techniques, the $\grave{a}$ trous algorithm based additive wavelet transformation (AWT) fusion techniques, the Principal Component analysis (PCA) fusion technique, Gram-Schmidt (GS) spectral sharpening, Pansharp, and the Smoothing Filter based Intensity Modulation (SFIM) fusion technique, for the fusion of a GeoEye-1 image. The results of the experiment show that the AWT fusion techniques maintain more spatial detail of the PAN image and spectral information of the MS image than other image fusion techniques. Also, the Pansharp technique maintains information of the original PAN and MS images as well as the AWT fusion technique.

Keywords

References

  1. Aanæs, H., J.R. Sveinsson, A.A. Nielsen, 2008. Model-based satellite image fusion, IEEE Transactions on Geoscience and Remote Sensing, 46(5): 1336-1346 https://doi.org/10.1109/TGRS.2008.916475
  2. Aiazzi, B., L. Alparone, S. Baronti, A. Garzelli, M. Selva, 2006. MTF-tailored multiscale fusion of high-resolution ms and pan imagery, Photogrammetric Engineering and Remote Sensing, 72(5): 591-596 https://doi.org/10.14358/PERS.72.5.591
  3. Aiazzi, B., S. Baronti, M. Selva, 2007. Improving component substitution pansharpening through multivariate regression of ms+pan data, IEEE Transactions on Geoscience and Remote Sensing, 45(10): 3230-3239 https://doi.org/10.1109/TGRS.2007.901007
  4. Alparone, L., S. Baronti, A. Garzelli, F. Nencini, 2004. A global quality measurement of pansharpened multispectral imagery, IEEE Transactions on Geoscience and Remote Sensing Letters, 1(4): 313-317 https://doi.org/10.1109/LGRS.2004.836784
  5. Chavex P. S. and A. Y. Kwarteng, 1989. Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis, Photogrammetric Engineering and Remote Sensing, 55(3): 339-348
  6. Chu, H. and W. Zhu, 2008. Fusion of IKONOS satellite imagery using IHS transform and local variation, IEEE Transactions on Geoscience and Remote Sensing Letters, 5(4): 653-657 https://doi.org/10.1109/LGRS.2008.2002034
  7. Dou, W., Y. Chen, X. Li, D.Z. Sui, 2007. A general framework for component substitution image fusion: An implementation using the fast image fusion method, Computer & Geosciences, 33(2): 219-228 https://doi.org/10.1016/j.cageo.2006.06.008
  8. Dutilleux, P. 1989. An implementation of the algorithms a trous to compute the wavelet transform, in wavelets: time-frequency methods and phase space. J. M. Cobbes, A. Grossman. and Ph. Tchamitchian, Eds. Berlin, Germany: Springer-Verlag, 298-304
  9. Filippidis, A., L. C. Jain, and N. Martin, 2000, Multisensor data fusion for surface land-mine detection, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 30(1): 145-150 https://doi.org/10.1109/5326.827491
  10. Garzelli, A. and F. Nencini, 2005. Interband structure modeling for pan-sharpening of very highresolution multispectral images, Information Fusion, 6: 213-224 https://doi.org/10.1016/j.inffus.2004.06.008
  11. Gonzalez-Audacana, M., J.L. Saleta, R. G. Catalan, R. Garcia, 2004, Fusion of multispectral and panchromatic images using improvement IHS and PCA mergers based on wavelet decomposition, IEEE Transactions on Geoscience and Remote Sensing, 42(6): 1291-1299 https://doi.org/10.1109/TGRS.2004.825593
  12. Gonzalez-Audacana, M., X. Otazu, O. Fors, J. Alvarez-Mozos, 2006. A low computationalcost method to fuse IKONOS images using the spectral response function of its sensors, IEEE Transactions on Geoscience and Remote Sensing, 44(6): 1683-1691 https://doi.org/10.1109/TGRS.2005.863299
  13. Hill, P., N. Canagarajah, D. Bull, Image fusion using complex wavelets, Proceeding of 13th British Machine Vision Conference, 2002. 487-496
  14. Huang, P. S. and T. Te-Ming, A target fusion based approach for classifying high spatial resolution imagery, Proceeding of Workshops Advances Techniques for Analysis of Remotely Sensed Data, 2003. 175-181
  15. Joshi, M. V. and L. Brunzzone, 2006. A model-based approach to multi-resolution fusion in remotely sensed images, IEEE Transactions on Geoscience and Remote Sensing, 44(9): 2549-2562 https://doi.org/10.1109/TGRS.2006.873340
  16. Kalpoma, K. and J. Kudoh, 2007. Image fusion processing for IKONOS 1m color imagery, IEEE Transactions on Geoscience and Remote Sensing, 45(10): 3075-3086 https://doi.org/10.1109/TGRS.2007.897692
  17. Khan, M. M., J. Chanussot, L. Condat, A. Montanvert, 2008. Indusion: fusion of multispectral and panchromatic images using the induction scaling technique. IEEE Transactions on Geoscience and Remote Sensing Letters, 5(1): 98-102 https://doi.org/10.1109/LGRS.2007.909934
  18. Laben, C. A. and B.V. Brower, Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. U.S. Patent 6011875, Jan. 4, 2000. Tech. Rep., Eastman Kodak Company
  19. Ling, Y., M. Ehlers, E. L. Usery, and M. Madden, 2007. FFT-enhaced IHS transform method for fusing high-resolution satellite images. ISPRS Journal of Photogrammetry and Remote Sensing, 61: 381-392 https://doi.org/10.1016/j.isprsjprs.2006.11.002
  20. Liu, J. G., 2000. Smoothing filter-based intensity modulation: A spectral preserve image fusion technique for improving spatial details, International Journal of Remote Sensing, 21(18): 3461-3472 https://doi.org/10.1080/014311600750037499
  21. Mallat, S., 1989. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7): 674-693 https://doi.org/10.1109/34.192463
  22. Murtagh, F. and J.L. Starck, 2000. Image processing through multiscale analysis and measurement noise modeling, Statistics and Computing, 10(2): 95-103 https://doi.org/10.1023/A:1008938224840
  23. Nikolakopoulos, K. G., 2008. Comparison of nine fusion techniques for very high resolution data, Photogrammetric Engineering and Remote Sensing, 74(5): 647-659 https://doi.org/10.14358/PERS.74.5.647
  24. Nunez, J., X. Otzau, O. Fors, A. Prades, V. Pala, R. Arbiol, 1999. Multiresolution-based image fusion with additive wavelet decomposition, IEEE Transactions on Geoscience and Remote Sensing, 37(3): 1204-1211 https://doi.org/10.1109/36.763274
  25. Otazu, X., Gonzalez-Audacana, M., O. Fors, J. Nunez, 2005. Introduction of sensor spectral response into image fusion methods, Application to wavelet-based methods, IEEE Transactions on Geoscience and Remote Sensing, 43(10): 2376-2385 https://doi.org/10.1109/TGRS.2005.856106
  26. Piella, G., 2003. A general framework for multiresolution image fusion: from pixels to regions, Information Fusion, 4: 259-280 https://doi.org/10.1016/S1566-2535(03)00046-0
  27. Pohl, C. and J. L. Van Genderen, 1998. Multisensor image fusion in remote sensing: Concepts, methods and applications, International Journal of Remote Sensing, 19(5): 823-854 https://doi.org/10.1080/014311698215748
  28. Ranchin, T. and L. Wald, 2000. Fusion of high spatial and spectral resolution images: The ARSIS concept and its implementation, Photogrammetric Engineering and Remote Sensing, 66(1): 49-61
  29. Shensa, M. J., 1992. The discrete wavelet transform: wedding the a trous and Mallat algorithm, IEEE Transactions on Signal Processing, 40(10): 2464-2482. https://doi.org/10.1109/78.157290
  30. Svab, A. and K. Ostir, 2006. High-resolution image fusion: methods to preserve spectral and spatial resolution, Photogrammetric Engineering and Remote Sensing, 72(5): 565-572 https://doi.org/10.14358/PERS.72.5.565
  31. Tania, S., 2008. Image fusion, Academic Press in an imprint of Elsevier, p. 51
  32. Thomas, C., T. Ranchin, L. Wald, and J. Chanussot, 2008. Synthesis of multispectral images to high spatial resolution: A critical review of fusion methods based on remote sensing physics, IEEE Transactions on Geoscience and Remote Sensing, 46(5): 1301-1312 https://doi.org/10.1109/TGRS.2007.912448
  33. Tu, T. M., W. C. Cheng, C. P. Chang, P. S. Huang, and J. C. Chang, 2007. Best tradeoff for highresolution image fusion to preserve spatial details and minimize color distortion, IEEE Transactions on Geoscience and Remote Sensing Letters, 4(2): 302-306 https://doi.org/10.1109/LGRS.2007.894143
  34. Tu, T. M., P. S. Huang, C. L. Hung, and C. P. Chang, 2004. A fast intensity-hue-saturation fusion technique with spectral adjustment for IKONOS imagery, IEEE Transactions on Geoscience and Remote Sensing Letters, 1(4). 309-312 https://doi.org/10.1109/LGRS.2004.834804
  35. Wald, L., 1999. Some terms of reference in data fusion, IEEE Transactions on Geoscience and Remote Sensing, 37(3): 1190-1193 https://doi.org/10.1109/36.763269
  36. Wang, A., D. Ziou, C. Armenakis, D. Li, and Q. Li, 2005. A comparative analysis of image fusion methods, IEEE Transactions on Geoscience and Remote Sensing, 43(6): 1391-1402 https://doi.org/10.1109/TGRS.2005.846874
  37. Zhang, Y., A new automatic approach for effectively fusing Landsat 7 as well as Ikonos images, Proceedings of IEEE/IGARSS'02, Toronto, Canada, 4; 2429-2431
  38. Zhang, Y., 2004. Understanding image fusion, Photogrammetric Engineering and Remote Sensing, 70(6): 657-661
  39. Zhang, Y. and G. Hong, 2005. An IHS and wavelet integrated approach to improve pansharpening visual quality of natural colour IKONOS and QuickBird images, Information Fusion, 6: 225-234 https://doi.org/10.1016/j.inffus.2004.06.009
  40. Zhou, J., D. L. Civco, and J. A. Silander, 1998. A wavelet transform method to merge Landsat TM and SPOT panchromatic data, International Journal of Remote Sensing, 19(4): 743-757 https://doi.org/10.1080/014311698215973