DOI QR코드

DOI QR Code

Generalized IHS-Based Satellite Imagery Fusion Using Spectral Response Functions

  • Kim, Yong-Hyun (Satellite Data Application Department, Korea Aerospace Research Institute) ;
  • Eo, Yang-Dam (Department of Advanced Technology Fusion, Konkuk University) ;
  • Kim, Youn-Soo (Satellite Data Application Department, Korea Aerospace Research Institute) ;
  • Kim, Yong-Il (Department of Civil and Environmental Engineering, Seoul National University)
  • Received : 2011.01.04
  • Accepted : 2011.02.18
  • Published : 2011.08.30

Abstract

Image fusion is a technical method to integrate the spatial details of the high-resolution panchromatic (HRP) image and the spectral information of low-resolution multispectral (LRM) images to produce high-resolution multispectral images. The most important point in image fusion is enhancing the spatial details of the HRP image and simultaneously maintaining the spectral information of the LRM images. This implies that the physical characteristics of a satellite sensor should be considered in the fusion process. Also, to fuse massive satellite images, the fusion method should have low computation costs. In this paper, we propose a fast and efficient satellite image fusion method. The proposed method uses the spectral response functions of a satellite sensor; thus, it rationally reflects the physical characteristics of the satellite sensor to the fused image. As a result, the proposed method provides high-quality fused images in terms of spectral and spatial evaluations. The experimental results of IKONOS images indicate that the proposed method outperforms the intensity-hue-saturation and wavelet-based methods.

Keywords

References

  1. C. Thomas et al., "Synthesis of Multispectral Images to High Spatial Resolution: A Critical Review of Fusion Methods Based on Remote Sensing Physics," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 5, 2008, pp. 1301-1312. https://doi.org/10.1109/TGRS.2007.912448
  2. M. Gonzalez-Audícana et al., "Fusion of Multispectral and Panchromatic Images Using Improved IHS and PCA Mergers Based on Wavelet Decomposition," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 6, 2004, pp. 1291-1299. https://doi.org/10.1109/TGRS.2004.825593
  3. B. Aiazzi et al., "A Comparison between Global and Context- Adaptive Pansharpening of Multispectral Images," IEE Geosci. Remote Sens. Lett., vol. 6, no. 2, 2009, pp. 302-306. https://doi.org/10.1109/LGRS.2008.2012003
  4. Z. Wang et al., "A Comparative Analysis of Image Fusion Methods," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 6, 2005, pp. 1391-1402. https://doi.org/10.1109/TGRS.2005.846874
  5. Y. Kim et al., "Improved Additive Wavelet Image Fusion," IEEE Geosci. Remote Sens. Lett., vol. 8, no. 2, 2011, pp. 263-267. https://doi.org/10.1109/LGRS.2010.2067192
  6. P.S. Pradhan et al., "Estimation of the Number of Decomposition Levels for a Wavelet-Based Multiresolution Multisensor Image Fusion," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 12, 2006, pp. 3674-3686. https://doi.org/10.1109/TGRS.2006.881758
  7. A. Garzelli, F. Nencini, and L. Capobianco, "Optimal MMSE Pan Sharpening of Very High Resolution Multispectral Images," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 1, 2008, pp. 228- 236. https://doi.org/10.1109/TGRS.2007.907604
  8. T.M. Tu et al., "A New Look at IHS-Like Fusion Methods," Inf. Fusion, vol. 2, 2001, pp. 177-186. https://doi.org/10.1016/S1566-2535(01)00036-7
  9. T.M. Tu et al., "A Fast Intensity-Hue-Saturation Fusion Technique with Spectral Adjustment for IKONOS Imagery," IEEE Geosci. Remote Sens. Lett., vol. 1, no. 4, 2004, pp. 309-312. https://doi.org/10.1109/LGRS.2004.834804
  10. Q. Du et al., "On the Performance Evaluation of Pan-Sharpening Techniques," IEEE Geosci. Remote Sens. Lett., vol. 4, no. 4, 2007, pp. 518-522. https://doi.org/10.1109/LGRS.2007.896328
  11. H. Zhao, G. Jia, and N. Li, "Transformation from Hyperspectral Radiance Data of Other Sensors Based on Spectral Superresolution," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 11, 2010, pp. 3903-3912.
  12. J. Franke, V. Heinzel, and G. Menz, "Assessment of NDVI Differences Caused by Sensor Specific Relative Spectral Response Functions," IEEE Geosci. Remote Sens. Symp., 2006, pp. 1138-1141.
  13. A. Svab and K. Ostir, "High-Resolution Image Fusion Methods to Preserve Spectral and Spatial Resolution," Photogramm. Remote Sens., vol. 72, no. 5, 2006, pp. 565-572. https://doi.org/10.14358/PERS.72.5.565
  14. X. Otazu et al., "Introduction of Sensor Response into Image Fusion Methods. Application to Wavelet-Based Methods," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 10, 2005, pp. 2376- 2385. https://doi.org/10.1109/TGRS.2005.856106
  15. T. Ranchin and L. Wald, "Fusion of High Spatial and Spectral Resolution Images: The Arsis Concept and Its Implementation," Photogramm. Remote Sens., vol. 66, no. 1, 2000, pp. 49-61.
  16. L. Alparone et al., "Comparison of Pansharpening Algorithms: Outcome of the 2006 GRS-S Data-Fusion Contest," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 10, 2007, pp. 3012-3021. https://doi.org/10.1109/TGRS.2007.904923
  17. M. Gonzalez-Audícana et al., "Comparison between Mallat's and the à Trous Discrete Wavelet Transform Based Algorithms for the Fusion of Multispectral and Panchromatic Images," Int J. Remote Sens., vol. 26, no. 3, 2005, pp. 595-614. https://doi.org/10.1080/01431160512331314056
  18. L. Alparone et al., "Multispectral and Panchromatic Data Fusion Assessment without Reference," Photogramm. Eng. Remote Sens., vol. 74, no. 2, 2008, pp. 193-200. https://doi.org/10.14358/PERS.74.2.193
  19. L. Alparone et al., "A Global Quality Measurement of Pan- Sharpened Multispectral Imagery," IEEE Geosci. Remote Sens. Lett., vol. 1, no. 4, 2004, pp. 313-317. https://doi.org/10.1109/LGRS.2004.836784
  20. Y. Zhang, "Methods for Image Fusion Quality Assessment - A Review, Comparison and Analysis," Proc. Int. Arch. Photogramm, Remote Sens. Spatial Inf. Sci., Beijing, China, vol. XXXVII, 2008, pp. 1101-1109.
  21. Z. Wang and A.C. Bovik, "A Universal Image Quality Index," IEEE Signal Process. Lett., vol. 9, no. 3, 2002, pp. 81-84. https://doi.org/10.1109/97.995823

Cited by

  1. Image Fusion With No Gamut Problem by Improved Nonlinear IHS Transforms for Remote Sensing vol.52, pp.1, 2011, https://doi.org/10.1109/tgrs.2013.2243157
  2. 고해상 광학센서의 스펙트럼 응답에 따른 영상융합 기법 비교분석 vol.30, pp.2, 2011, https://doi.org/10.7780/kjrs.2014.30.2.6
  3. Bilateral Filtering-Based Enhanced Pansharpening of Multispectral Satellite Images vol.11, pp.11, 2011, https://doi.org/10.1109/lgrs.2014.2314389
  4. A Review of Quality Metrics for Fused Image vol.4, pp.None, 2011, https://doi.org/10.1016/j.aqpro.2015.02.019
  5. Fast and Efficient Satellite Imagery Fusion Using DT-CWT Proportional and Wavelet Zero-Padding vol.33, pp.6, 2011, https://doi.org/10.7848/ksgpc.2015.33.6.517
  6. Image Fusion of Spectrally Nonoverlapping Imagery Using SPCA and MTF-Based Filters vol.14, pp.12, 2011, https://doi.org/10.1109/lgrs.2017.2762427
  7. A Hybrid Pansharpening Algorithm of VHR Satellite Images that Employs Injection Gains Based on NDVI to Reduce Computational Costs vol.9, pp.10, 2011, https://doi.org/10.3390/rs9100976
  8. Remote sensing data fusion using fruit fly optimization vol.80, pp.2, 2011, https://doi.org/10.1007/s11042-020-09798-2
  9. Remote sensing data fusion using fruit fly optimization vol.80, pp.2, 2011, https://doi.org/10.1007/s11042-020-09798-2