IHS based Pan Sharpening with Geo-Image Processing

Authors

  • Alpesh R. Sankaliya  Electronics & Communication Engineering Department, Government Polytechnic, Dahod, Gujarat, India

Keywords:

Geo-Image Processing, Pan-Sharpening, Grayscale image, CMY, IHS, HSL, HSV, Image Fusion, JAI

Abstract

The image processing methodologies that have been actively studied and developed now play a very significant role in the flourishing Genomic Image Processing research. Spatial data are captured by some remote sensing satellites. These satellites actually capture many bands of images. Now, these captured images are put under different types of processing in order to get the useful image for further usage. The main advantage of doing processing is when a client needs to purchase these images; a provider cannot give the data as it is taken. It has to be converted in the required format for client usage. Moreover, images once captured it may be possible that it cannot be captured again in future, so there will more need to process such data for future usage and work. Using IHS (Intensity-Hue Saturation) based Pan Sharpening we can enhance these images. Pan-sharpening is to fuse a low spatial resolution multispectral image with a higher resolution panchromatic image to obtain an image with high spectral and spatial resolution.

References

  1. M. Choi, R. Kim. “Fusion of Multispectral and Panchromatic Satellite Images Using the Curvelet Transforms.” IEEE Geosci. Remote Sens. Lett. Vol. 2, No. 2, pp. 136-140, Apr. 2005.
  2. Zhang J., 2010. “Multi-Source Remote Sensing Data Fusion: Status and Trends”. International Journal of  Image and Data Fusion, Vol. 1, No. 1, March 2010, pp.5–24.
  3. Ehlers M., Klonus S., Johan P., strand Ǻ and Rosso P., 2010. “Multi-Sensor Image Fusion For Pan Sharpening In Remote Sensing”. International Journal of Image and Data Fusion,Vol. 1, No. 1, March 2010, pp.25–45.
  4. Ehlers M., Klonus S., Johan P., strand Ǻ and Rosso P., 2010. “Multi-Sensor Image Fusion For Pan Sharpening In Remote Sensing”. International Journal of Image and Data Fusion,Vol. 1, No. 1, March 2010, pp.25–45.
  5. Gangkofner U. G., P. S. Pradhan, and D. W.Holcomb, 2008. “Optimizing the High-Pass Filter Addition Technique for Image Fusion”. Photogrammetric Engineering & Remote Sensing, Vol. 74, No. 9, pp. 1107–1118.
  6. Q. Du, O. Gungor, and J. Shan. “Performance Evaluation for Pan-sharpening Techniques.” Department of Electrical and computer engineering, Mississippi State University. 24. 2008.
  7. L. Wald. “Quality of High Resolution Synthesized Images: Is There a Simple Criterion?” Proc. Int. Conf. Fusion Earth Data. 2000.
  8. V. P. Shah and N. H. Younan. “An Efficient Pan-Shapening Method via a Combined Adaptive PCA Approach and Contourlets.” IEEE Trans. on Geosc. and Remote Sens. 46. 2008.
  9. X. Otazu and M. Gonzalez-Ausicana. “Introduction of Sensor Spectral Response Into Image Fusion Methods: Application to Wavelet-Based Methods.” IEEE Transactions of Geoscience and Remote Sens. vol. 43, pp. 2376-2385, 2005.
  10. A. Eshtehari and H. Ebadi. “Image Fusion of Landsat ETM+and Spot Satellite Images Using IHS, Brovey and PCA.” Toosi University of Technology. 2008.
  11. C. Ballester, V. Caselles, L. Igual and J. Verdera. “A Variational Model for P+XS Image Fusion.” International Journal of Computer Vision, pp.43-58, 2006.
  12. P. Perona and J. Malik. “Scale-space and Edge Detection Using Anisotropic Diffusion,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 12, no. 7, pp. 629-639, Jul. 1990.
  13. H. Aanaes and J. Sveinsson, “Model-based Satellite Image Fusion,” IEEE Trans. Geosci. Remote Sens. Vol. 46, no. 5,
  14. A. Goetz, W. Boardman and R. Yunas. “Discrimination Among Semi-Arid Landscape Endmembers Using the Spectral Angle Mapper(SAM) Algorithm.” Proc. Summeries 3rd Annu. JPL Airborne Geosci Workshop. pp. 147-149. 1992.
  15. Y. Zhang. “Understanding Image Fusion.” PCI Geomatics. 2008.
  16. M. Choi. “A New Intensity-Hue-Saturation Fusion Approach to Image Fusion with a Tradeoff Parameter.” IEEE Transactions of Geoscience and Remote Sensing. Vol. 44, No. 6, pp. 1672-1682, Jun. 2006.
  17. M. Choi, H. Kim, N.I. Cho and H.O. Kim. “An Improved Intensity-Hue-Saturation Method for IKONOS Image Fusion.” International Journal of Remote Sensing. 2008.
  18. J. Canny. “A Computational Approach to Edge Detection,” IEEE Trans. Pattern Anal. Mach. Intell.,vol. PAMI-8, no.6, pp.679-698, Nov. 1986.
  19. C. Chang. “Spectral Information Divergence for Hyperspectral Image Analysis” Proc. Geosci. Remote Sens. Symp. Vol. 1. pp. 509-511, 1999.
  20. A. Bovik and Z. Wang. “A Universal Image Quality Index.” IEEE Signal Processing Letters. Vol. 9, No. 3, pp. 81-84, Mar. 2002.
  21. L. Alparone, B. Aiazzi, S. Baronti, A. Garzelli and P. Nencini. "A Global Quality Measurement of Pan-sharpened Multispectral Imagery.” IEEE Geosci. Remote Sens. Lett. Vol. 1, No. 4, pp. 313-317, Oct. 2004.
  22. Lu J., Zhang B., Gong Z., Li Z., Liu H., 2008. “The Remote-Sensing Image Fusion Based On GPU”. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing pp. 1233-1238.
  23. Hui Y. X. and Cheng J. L., 2008. “Fusion Algorithm for Remote Sensing Images Based on Nonsubsampled Contourlet Transform”. ACTA AUTOMATICA SINICA, Vol. 34, No. 3.pp. 274-281.
  24. Hsu S. L., Gau P.W., Wu I L., and Jeng J.H., 2009. “Region-Based Image Fusion with Artificial Neural Network”. World Academy of Science, Engineering and Technology, 53, pp 156 -159.

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2015-10-30

Issue

Volume 1, Issue 5, September-October-2015

Section

Research Articles

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How to Cite

[1]
Alpesh R. Sankaliya, " IHS based Pan Sharpening with Geo-Image Processing, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 1, Issue 5, pp.393-398, September-October-2015.