DOI QR코드

DOI QR Code

Comparison of Image Fusion Methods to Merge KOMPSAT-2 Panchromatic and Multispectral Images

KOMPSAT-2 전정색영상과 다중분광영상의 융합기법 비교평가

  • 오관영 (서울시립대학교 공간정보공학과) ;
  • 정형섭 (서울시립대학교 공간정보공학과) ;
  • 이광재 (한국항공우주연구원)
  • Received : 2011.11.28
  • Accepted : 2012.01.17
  • Published : 2012.02.29

Abstract

The objective of this study is to propose efficient data fusion techniques feasible to the KOMPSAT-2 satellite images. The most widely used image fusion techniques, which are the high-pass filter (HPF), the intensity-hue-saturation-based (modified IHS), the pan-sharpened, and the wavelet-based methods, was applied to four KOMPSAT - 2 satellite images having different regional and seasonal characteristics. Each fusion result was compared and analyzed in spatial and spectral features, respectively. Quality evaluation of image fusion techniques was performed in both quantitative and visual analysis. The quantitative analysis methods used for this study were the relative global dimensional error (spatial and spectral ERGAS), the spectral angle mapper index (SAM), and the image quality index (Q4). The results of quantitative and visual analysis indicate that the pan-sharpened method among the fusion methods used for this study relatively has the suitable balance between spectral and spatial information. In the case of the modified IHS method, the spatial information is well preserved, while the spectral information is distorted. And also the HPF and wavelet methods do not preserve the spectral information but the spatial information.

본 연구의 목적은 KOMPSAT-2 위성영상에 가장 일반적으로 적용 가능한 영상융합기법을 제시하는 것이다. 가장 널리 사용되는 영상융합기법인 HPF, modified IHS, pan-sharpened, wavelet을 지역적, 계절적 특성이 서로 다른 4장의 KOMPSAT-2 위성영상에 적용하였고, 각각의 융합결과를 공간적, 분광적으로 비교분석하였다. 영상융합기법의 품질평가는 시각적 분석과 정량적 분석을 병행하여 수행하였으며, 정량적 분석에는 spatial ERGAS, spectral ERGAS, SAM, Q4가 사용되었다. 종합적인 분석결과를 고려할 때, pan-sharpened가 색상정보와 공간정보의 균형적인 보존 측면에서 다른 융합기법들에 비해, 상대적으로 우수한 결과를 나타냈다. modified-IHS의 경우, 공간정보는 잘 보존하였지만 다소 큰 색상 왜곡이 발생되었고, HPF와 wavelet은 색상 왜곡은 적었지만, 공간정보의 왜곡이 발생하였다.

Keywords

References

  1. 김용현, 김윤수, 2009. Fast IHS 변환을 이용한 trade-off 영상 융합기법, 항공우주기술학회지, 8(2): 26-32.
  2. 최재완, 김형태, 2008. 수정된 영상유도 기법을 통한 융합영상의 분광정보 향상 알고리즘, 한국지형공간정보학회지, 16(3): 15-20.
  3. Alparone, L., S. Baronti, A. Garzelli, and F. Nencini, 2004. A global quality measurement of pan-sharpened multispectral imagery, IEEE Transactions on Geoscience and Remote Sensing, 1(4): 313-317. https://doi.org/10.1109/LGRS.2004.836784
  4. Alparone, L., L. Wald, J. Chanussot, C. Thomas, P. Gamba, and L.M. Bruse, 2007. Comparison of algorithms: Outcome of the 2006 GRS-S Data-Fusion contest, IEEE Transactions on Geoscience and Remote Sensing, 45(10): 219-228.
  5. Chavez, P.S., S.C. Sides, and J.A. Anderson, 1991. Comparison of three different method to merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic, Photogrammetric Engineering & Remote Sensing, 57(3): 295-303.
  6. Choi, M., 2006. A new intensity-hue-saturation fusion approach to image fusion with a tradeoff parameter, IEEE Transactions on Geoscience and Remote Sensing, 44(6): 167-168. https://doi.org/10.1109/TGRS.2005.859357
  7. Gangkofner, U. and D. Holcomb, 2008. HP Resolution Merge, In: ERDAS IMAGINE Help, Leica Geosystems Geospatial Imaging, LLC, 2008.
  8. King, R.L. and J.W. Wang, 2001. A wavelet based algorithm for pan sharpening Landsat 7 imagery, IEEE International Geoscience and Remote Sensing Symposium, Sydney, Australia, July 9-13, 2001, 849-851.
  9. Klonus, S. and M. Ehlers, 2009. Performance of evaluation methods in image fusion, International Conference on Information Fusion, Seattle, USA, July 6-9, 2009, 1409-1416.
  10. Kruse, F.A., J.W. Boardman, A.B. Lefkoff, K.B. Heidebrecht, A.T. Shapiro, P.J. Barloon, and A.F.H. Goetz, 1993. The Spectral Image Processing System (SIPS): Interactive visualization and analysis of imaging spectrometer data. Remote Sensing of Environment 44: 145-163. https://doi.org/10.1016/0034-4257(93)90013-N
  11. Saacedra, M.L. and C. Gonzalo, 2006. Spectral or spatial quality for fused satellite imagery? A trade-off solution using the wavelet a'trous algorithm, International Journal of Remote Sensing, 27(7): 1453-1364. https://doi.org/10.1080/01431160500462188
  12. Ranchin, T. and L. Wald, 2000. Fusion of high spatial and resolution images: the ARSIS concept and its implementation, Photogrammetric Engineering & Remote Sensing, 66(1): 49-61.
  13. Sangwine, S.J. and T.A. Ell, 2000. Color image filters based on hypercomplex convolution, IEEE Vision, Image and Signal Processing, 147(2): 89-93. https://doi.org/10.1049/ip-vis:20000211
  14. Siddiqui, Y., 2003. The modified IHS method for fusing satellite imagery, American Society for Photogrammety and Remote Sensing, Anchorage, USA, May. 5-9, 2003, in CD.
  15. Vrabel, J., 1996. Multispectral imagery band sharpening study, Photogrammetric Engineering & Remote Sensing, 62(9): 1075-1083.
  16. Thomas, C. and L. Wald, 2006. Comparing distances for quality assessment of fused images, European Association of Remote Sensing Laboratories, Warsaw, Poland, May 29 - June 2, 2006: 101-111.
  17. Wald, L., 2000. Quality of high resolution synthesized: is there a simple criterion?, International Conference on Fusion of Earth Data, Sophia Antipolis, France, Jan 26-28, 2000, 99-103.
  18. Wang, Z. and A.C. Bovik, 2002. A universal image quality index, IEEE Signal Processing Letters, 9(3): 81-84. https://doi.org/10.1109/97.995823
  19. Wang, Z., D. Ziou, C. Armenakis, D. Li, and Q. Li, 2005. A comparative Analysis of Image Fusion Methods, IEEE Transactions on Geoscience and Remote Sensing, 43(6): 1391-1402. https://doi.org/10.1109/TGRS.2005.846874
  20. Yakhdani, M.F. and A. Azizi, 2010. Quality assessment of image fusion techniques for multisensor high resolution satellite images(case study: IRS-P5 and IRS-P6 satellite images), International Society for Photogrammetry and Remote Sensing, Vienma, Austria, July 5-7, 2010, 204-209.
  21. Yocky, D.A., 1996. Multiresolution Wavelet Decomposition Image Merger of Landsat Thematic Mapper and SPOT Panchromatic Data. Photogrammetric Engineering & Remote Sensing, 62(9): 1067-1074.
  22. Zhang, Y., 2002. A new automatic approach for effectively fusing Landsat-7 as well as IKONOS images, IEEE International Geoscience and Remote Sensing Symposium, Toronto, Canada, June 24-28, 2002, 2429-2431.

Cited by

  1. Comparative Analysis of Image Fusion Methods According to Spectral Responses of High-Resolution Optical Sensors vol.30, pp.2, 2014, https://doi.org/10.7780/kjrs.2014.30.2.6
  2. Comparison Analysis of Quality Assessment Protocols for Image Fusion of KOMPSAT-2/3/3A vol.32, pp.5, 2016, https://doi.org/10.7780/kjrs.2016.32.5.5
  3. Research Trend Analysis of Geospatial Information in South Korea Using Text-Mining Technology vol.2017, pp.1687-7268, 2017, https://doi.org/10.1155/2017/2765256
  4. 아리랑위성 2호 한반도 정사모자이크영상 제작 vol.16, pp.3, 2013, https://doi.org/10.11108/kagis.2013.16.3.103
  5. KOMPSAT-2 영상과 항공 LiDAR 자료를 이용한 3차원 해안선 매핑 vol.33, pp.1, 2012, https://doi.org/10.7848/ksgpc.2015.33.1.23