대한원격탐사학회지 (Korean Journal of Remote Sensing)

  • 제24권3호
  • /
  • Pages.257-266
  • /
  • 2008
  • /
  • 1225-6161(pISSN)
  • /
  • 2287-9307(eISSN)
대한원격탐사학회 (Korean Society of Remote Sensing)
권호 표지
DOI QR코드

DOI QR Code

Quadratic Programming Approach to Pansharpening of Multispectral Images Using a Regression Model

  • 발행 : 2008.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

This study presents an approach to synthesize multispectral images at a higher resolution by exploiting a high-resolution image acquired in panchromatic modality. The synthesized images should be similar to the multispectral images that would have been observed by the corresponding sensor at the same high resolution. The proposed scheme is designed to reconstruct the multispectral images at the higher resolution with as less color distortion as possible. It uses a regression model of the second order to fit panchromatic data to multispectral observations. Based on the regression model, the multispectral images at the higher spatial resolution of the panchromatic image are optimized by a quadratic programming. In this study, the new method was applied to the IKONOS 1m panchromatic and 4m multispectral data, and the results were compared with them of several current approaches. Experimental results demonstrate that the proposed scheme can achieve significant improvement over other methods.

키워드

참고문헌

  1. Aiazzi, B., L. Alparone, S. Baronti, and A. Garzelli, 2002. Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis, IEEE Trans. Geosci. Remote Sens., 40: 2300-2312 https://doi.org/10.1109/TGRS.2002.803623
  2. Alparone, L., L. Wald, J. Chanussot, C. Thomas, P. Gamba, and L. M. Bruce, 2007. Comparison of pansharpening algoriths: Outcome of the 2006 GRS-S data-fusion contest, IEEE Trans. Geosci. Remote Sensing, 45: 3012-3021 https://doi.org/10.1109/TGRS.2007.904923
  3. Chavez, P. S., S. C. Sildes, and J. A. Anderson, 1991. Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic, Photogramm. Eng. Rem. Sens., 57: 295-303
  4. Civco, D. L., Y. Wang, and J. A. Silander, 1995. Characterizing forest ecosystems in Connecticut by integrating Landsat TM and SPOT panchromatic data, Proc.1995 Annual ASPRS/ACSM Convention, Charlotte, NC., 2: 216-224
  5. Gill, P. E., W. Murray, and M. H. Wright, 1991. Numerical Linear Algebra and Optimization, Vol. 1, Addison Wesley
  6. Laben, C. A. and B. V. Brower, 2000. Process for enhancing the spatial resoltion of multispectral imagery using pan-sharpening, Technical Report US Patent #6,110,875, Eastman Kodak Company
  7. Mallet, S. G., 1989. A theory for multiresolution signal decomposition: the wavelet representation, IEEE Trans.Pattern Anal. Mach. Intel., 11: 674-693 https://doi.org/10.1109/34.192463
  8. Nunez, J., X. Otazu, O. Fors, A. Prades, V. Pala, and R. Arbiol, 1999. Multiresolution-based image fusion with additive wavelet decomposition, IEEE Trans. Geosci. Remote Sens., 37: 1204-1211 https://doi.org/10.1109/36.763274
  9. Otazu, X., M. Gonzales Audicana, O. Fors, and J. Nunez, 2005. Introduction of sensor spectral response into image fusion methods: Application to wavelet-based methods, IEEE Trans. Geosci. Remote Sens., 43: 2376-2385 https://doi.org/10.1109/TGRS.2005.856106
  10. Wald, L., T. Ranchin, and M. Mangolini, 1997. Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images, Photogramm. Eng. Remote Sens., 63: 691-699
  11. Yocky, D. A., 1996. Multiresolution wavelet decomposition image merger of landsat thematic mapper and spot panchromatic data, Photogramm. Eng. Rem. Sens., 69: 1067-1074
  12. Zhang, Y and G. Hong, 2005. An IHS and wavelet integrated approach to improve pansharpening visual quality of natural colur IKONS and QuickBird images, Information Fusion, 6: 225-234 https://doi.org/10.1016/j.inffus.2004.06.009