Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Pansharpening of Multispectral Satellite Images via Lattice Structures

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
N.H. Kaplan, I. Erer

N H Kaplan and I Erer. Article: Pansharpening of Multispectral Satellite Images via Lattice Structures. International Journal of Computer Applications 140(7):9-14, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {N.H. Kaplan and I. Erer},
	title = {Article: Pansharpening of Multispectral Satellite Images via Lattice Structures},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {140},
	number = {7},
	pages = {9-14},
	month = {April},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


Since satellite images with high spatial and spectral quality are highly desired for remote sensing applications, various algorithms have been developed for the fusion of multispectral and panchromatic images. Wavelet transform based mergers have found enormous interest in the fusion community. This paper introduces undecimated filterbanks with lattice structure and applies them to the pansharpening problem. Multispectral and panchromatic images are decomposed using the developed lattice analysis structure into subbands which are combined by using a predefined fusion rule. The fused image is obtained by the inverse lattice filtering of the fused subbands. Fusion results and quality metrics show that the proposed method can be a good alternative to the other well-known pansharpening methods.


  1. Piella, G. 2002. A General framework for multiresolution image fusion: from pixels to regions. Information Fusion, 4(4), 1386-3711.
  2. Pohl, C. and Van Genderen J. L. 1998. Multisensor image fusion in remote sensing: concepts, methods and applications. Int. Journal of Remote Sensing, 99(5), 823-854.
  3. Chavez, P. S., Stuart, J., Sides, C. and Anderson J. A. 1991. Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic. Photogrammetric Engineering and Remote Sensing, 57(14), 259–303..
  4. Carper, W. J., Lilesand, T. W. and Kieffer, R.W. 1990. The use of Intensity-Hue-Saturation transformation for merging SPOT panchromatic and multispectral image data. Photogrammetric Engineering and Remote Sensing, 56(4), 459-467.
  5. Tu, T. M., Huang, P. S., Huang, C. L. and Chang, C. P. 2004. A fast intensity-hue-saturation fusion technique with spectral adjustement for IKONOS imagery. IEEE Trans. Geosci. Remote Sens. Lett., 1(4), 309-312.
  6. Choi, M. 2006.A new intensity-hue-saturation approach to image fusion with a tradeoff parameter. IEEE Trans. Geosci, Remote Sens., 44(6), 1672-1682.
  7. Chavez, P. S. and Kwarteng, A.Y. 1989. Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis. Photogrammetric Engineering and Remote Sensing, 55, 339–348.
  8. Mallat, S.G. 1989. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Machine Intell., 11(7), 674–693.
  9. Pajares, G. and dela Cruz, J. M. 2004. A wavelet-based image fusion tutorial. Pattern Recognition, 37(9), 1855-1872.
  10. Li, S., Kwok, J. T. and Wang, Y. 2002. Using The discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images. Information Fusion, 3(1), 17-23.
  11. Li, S., Yang, B. and Hu, J. Performance comparison of different multiresolution transforms for image fusion. Information Fusion, 12(2), 74-84.
  12. Vaidyanathan, P. and Hoang, P-Q. 1988. Lattice structures for optimal design and robust implementation of two-channel perfect reconstruction QMF Banks. IEEE Trans. ASSP, 36(1), 81-94.
  13. Sezen, S. and Ertüzün, A. 2006. 2D Four-channel perfect reconstruction filter bank realized with the 2D lattice filter structure. EURASIP Journal on Applied Signal Processing, 2006, 1–16.
  14. Eskicioglu, A. and Fisher, P. 1995. Image Quality measures and their performance. IEEE Trans. On Comm., 43(12), 2959-2965.
  15. Alperone, L., Wald, L., Chanussot, J., Thomas, C., Gamba, P. and Mann Bruce, L. 2007. Comparison of pansharpening algoritms: outcome of the 2006 GRS-S data-fusion contest. IEEE trans Geosci. Remote Sens. 45(10), 3012-3021.
  16. Wald, L., Ranchin, T. and Mangolini, M. 1997. Fusion of satellite images of different spectral resolutions: Assessing the quality of resulting images. Photogramm. Eng. Remote Sens., 63(6), 691-699.
  17. Lillo-Saavedra, M., Gonzalo, C., Arquero, A. and Martinez, E. 2005. Fusion of multispectral and panchromatic satellite sensor imagery based on tailored filtering in the Fourier domain. International Journal of Remote Sensing. 26(6), 1263-1268.
  18. Bovik, A. and Wang, Z. 2002. A universal image quality index. IEEE Signal Proces. Lett, 9(3), 81-84.


Image fusion, pansharpening, multispectral images, multiresolution analysis, lattice filters.