Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
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Image Fusion Methods for Multispectral and Panchromatic Images of Pleiades and KOMPSAT 3 Satellites

  • Kim, Yeji (Dept. of Civil and Environmental Engineering, Seoul University) ;
  • Choi, Jaewan (School of Civil Engineering, Chungbuk National University) ;
  • Kim, Yongil (Dept. of Civil Engineering, Dept. of Civil and Environmental Engineering, Seoul University)
  • Received : 2018.10.13
  • Accepted : 2018.10.24
  • Published : 2018.10.31
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Abstract

Many applications using satellite data from high-resolution multispectral sensors require an image fusion step, known as pansharpening, before processing and analyzing the multispectral images when spatial fidelity is crucial. Image fusion methods are to improve images with higher spatial and spectral resolutions by reducing spectral distortion, which occurs on image fusion processing. The image fusion methods can be classified into MRA (Multi-Resolution Analysis) and CSA (Component Substitution Analysis) approaches. To suggest the efficient image fusion method for Pleiades and KOMPSAT (Korea Multi-Purpose Satellite) 3 satellites, this study will evaluate image fusion methods for multispectral and panchromatic images. HPF (High-Pass Filtering), SFIM (Smoothing Filter-based Intensity Modulation), GS (Gram Schmidt), and GSA (Adoptive GS) were selected for MRA and CSA based image fusion methods and applied on multispectral and panchromatic images. Their performances were evaluated using visual and quality index analysis. HPF and SFIM fusion results presented low performance of spatial details. GS and GSA fusion results had enhanced spatial information closer to panchromatic images, but GS produced more spectral distortions on urban structures. This study presented that GSA was effective to improve spatial resolution of multispectral images from Pleiades 1A and KOMPSAT 3.

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References

  1. Aiazzi, B., Alparone, L., Baronti, S., Garzelli, A., and Selva, M. (2006), MTF-tailored multiscale fusion of high-resolution MS and Pan imagery, Photogrammetric Engineering and Remote Sensing , Vol. 72, No. 5, pp. 591-596. https://doi.org/10.14358/PERS.72.5.591
  2. Aiazzi, B., Baronti, S., and Selva, M. (2007), Improving component substitution pansharpening through multivariate regression of MS + Pan data, IEEE Transactions on Geoscience and Remote Sensing , Vol. 45, No. 10, pp. 3230-3239. https://doi.org/10.1109/TGRS.2007.901007
  3. Aiazzi, B., Baronti, S., Lotti, F., and Selva, M. (2009), A comparison between global and context-adaptive pansharpening of multispectral images, IEEE Geoscience and Remote Sensing Letters , Vol. 6, No. 2, pp. 302-306. https://doi.org/10.1109/LGRS.2008.2012003
  4. Aiazzi, B., Alparone, L., Baronti, S., Carla, R., Garzelli, A., and Santurri, L. (2017), Sensitivity of pansharpening methods to temporal and instrumental changes between multispectral and panchromatic data sets, IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, No. 1, pp. 308-319. https://doi.org/10.1109/TGRS.2016.2606324
  5. Chavez, P., Sides, S.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 , Vol. 57, No. 3, pp. 295-303.
  6. Choi, J., Yu, K., and Kim, Y. (2011), A new adaptive component-substitution-based satellite image fusion by using partial replacement, IEEE Transactions on Geoscience and Remote Sensing , Vol. 49, No. 1, pp. 295-309. https://doi.org/10.1109/TGRS.2010.2051674
  7. Choi, J., Yeom, J., Chang, A., Byun, Y., and Kim, Y. (2013), Hybrid pansharpening algorithm for high spatial resolution satellite imagery to improve spatial quality, IEEE Geoscience and Remote Sensing Letters , Vol. 10, No. 3, pp. 490-494. https://doi.org/10.1109/LGRS.2012.2210857
  8. DigitalGlobe. (2013), Spectral response for DigitalGlobe earth imaging instruments, DigitalGlobe Technical Information, https://dg-cms-uploads-production.s3.amazonaws.com/uploads/document/file/105/DigitalGlobe_Spectral_Response_1.pdf (last date accessed: 9 September 2018).
  9. Dong, L. and Shan, J. (2013), A comprehensive review of earthquake-induced building damage detection with remote sensing techniques, ISPRS Journal of Photogrammetry and Remote Sensing , Vol. 84, pp. 85-99. https://doi.org/10.1016/j.isprsjprs.2013.06.011
  10. Kim, Y., Choi, J., Han, D., and Kim, Y. (2015), Blockbased fusion algorithm with simulated band generation for hyperspectral and multispectral images of partially different wavelength ranges, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , Vol. 8, No. 6, pp. 2997-3007. https://doi.org/10.1109/JSTARS.2015.2433673
  11. Lee, M.S., Kim, S.J., Shin, H.S., Park, J.K., and Park, J.H. (2009), Extraction of agricultural land use and crop growth information using KOMPSAT-3 resolution satellite image, Korean Journal of Remote Sensing , Vol. 25, No. 5, pp. 411-421. (in Korean with English abstract)
  12. Liu, J.G. (2000), Smoothing filter-based intensity modulation: A spectral preserve image fusion technique for improving spatial details, International Journal of Remote Sensing , Vol. 21, No. 18, pp. 3461-3472. https://doi.org/10.1080/014311600750037499
  13. Oh, K., Jung, H., and Lee, K. (2012), Comparison of image fusion method to merge KOMPSAT-2 panchromatic and multispectral images, Korea Journal of Remote Sensing , Vol. 28, No. 1, pp. 39-54. (in Korean with English abstract) https://doi.org/10.7780/kjrs.2012.28.1.039
  14. Ranchin, T., and Wald, L. (2000), Fusion of high spatial and spectral resolution images: the ARSIS concept and its implementation, Photogrammetric Engineering and Remote Sensing , Vol. 66, No. 1, pp. 49-61.
  15. Stathaki, T. (2011), Image Fusion: Algorithms and Applications , Elsevier, New York, USA.
  16. Vivone, G., Alparone, L., Chanussot, J., Dalla Mura, M., Garzelli, A., Licciardi, G.A., Restaino, R., and Wald, L. (2015), A critical comparison among pansharpening algorithms., IEEE Transactions on Geoscience and Remote Sensing , Vol. 53, No. 5, pp. 2565-2586. https://doi.org/10.1109/TGRS.2014.2361734
  17. Wald, L. (2000), Quality of high resolution synthesized images: Is there a simple criterion?, International Conference of Fusion Earth Data-2000 , SEE/URISCA, 26-28, January, Sophia Antipolis, France, pp. 99-103.
  18. Wald, L., Ranchin, T., and Mangolini, M. (1997), Fusion of satellite images of different spatial resolutions: assessing the quality of resulting images, Photogrammetric Engineering and Remote Sensing , Vol. 63, No. 6, pp. 691-699.
  19. Wang, Z. and Bovik, A.C. (2002), A universal image quality index, IEEE Signal Processing Letters , Vol. 9, No. 3, pp. 81-84. https://doi.org/10.1109/97.995823