영상의 분광 및 공간 특성을 이용한 고해상도 위성영상 융합 알고리즘

Pan-Sharpening Algorithm of High-Spatial Resolution Satellite Image by Using Spectral and Spatial Characteristics

  • 최재완 (서울대학교 공과대학 건설환경공학부 공간정보연구실) ;
  • 김용일 (서울대학교 공과대학 건설환경공학부)
  • 투고 : 2010.05.06
  • 심사 : 2010.06.02
  • 발행 : 2010.06.30

초록

일반적으로, 영상 융합은 서로 다른 특징을 가지는 2개 이상의 영상을 이용하여 각 영상의 장점 및 특징을 모두 가지는 하나의 영상으로 재구성하는 것을 의미한다. 특히, 원격탐사 분야에서의 영상융합은 멀티스펙트럴 영상의 공간해상도를 향상시키는 것을 의미하며 이러한 이유로 인하여 Pan-sharpening 기술로도 불리어진다. 특히, 융합영상은 변화탐지, 영상 지도 제작, 도시 분석 등 다양한 분야에 적용 가능하기 때문에 중요성이 증대되고 있다. 그러나, 기존에 제안된 알고리즘들은 멀티스펙트럴 영상의 분광정보를 왜곡시키거나, 융합 영상의 공간해상도가 흑백영상의 공간해상도에 비하여 저하되는 문제를 지닌다. 이를 위해 본 논문에서는 멀티스펙트럴 영상의 분광 및 공간특성을 고려한 새로운 융합 방법론을 제안하였다. 본 알고리즘의 평가를 위해서 KOMPSAT-2, QuickBird 위성영상에 알고리즘을 적용을 하였으며, 기존의 영상융합 알고리즘에 비하여 공간적/분광적인 측면에서 모두 향상된 결과를 보임을 확인할 수 있었다.

Generally, image fusion is defined as generating re-organized image by merging two or more data using special algorithms. In remote sensing, image fusion technique is called as Pan-sharpening algorithm because it aims to improve the spatial resolution of original multispectral image by using panchromatic image of high-spatial resolution. The pan-sharpened image has been an important task due to various applications such as change detection, digital map creation and urban analysis. However, most approaches have tended to distort the spectral information of the original multispectral data or decrease the spatial quality compared with the panchromatic image. In order to solve these problems, a novel pan-sharpening algorithm is proposed by considering the spectral and spatial characteristics of multispectral image. The algorithm is applied to the KOMPSAT-2 and QuickBird satellite image and the results showed that our method can improve the spectral/spatial quality compared with the existing fusion algorithms.

키워드

참고문헌

  1. 최재완, 김형태, 2008, "수정된 영상 유도 기법을 통한융합영상의 분광정보 향상 알고리즘", 지형공간정보, 한국지형공간정보학회지, 제 16권, 3호, pp. 15-20.
  2. B. Aiazzi, L. Alparone, S. Baronti, A. Garzeli, and M.Selva, 2006, "MTF-tailored multiscale fusion ofhigh-resolution MS and Pan imagery", Photogramm. Eng. Remote Sens., Vol.72, No.5, pp.591-596. https://doi.org/10.14358/PERS.72.5.591
  3. Aiazzi, S. Baronti, F. Lotti, and M. Selva, 2009,"A comparison between global and context-adaptivepansharpening of multispectral images", IEEE Transactions on Geoscience and Remote SensingLetters, Vol.6, No.2, pp.302-306. https://doi.org/10.1109/LGRS.2008.2012003
  4. C. A. Laben, and B. V. Brower, 2000, "Process forenhancing the spatial resolution of multispectralimagery using pan-sharpening", U.S. Patent6011875, Tech. Rep., Eastman Kodak Company.
  5. J. Lee, and C. Lee, 2010, "Fast and EfficientPanchromatic Sharpening", IEEE Transactions onGeoscience and Remote Sensing, Vol.48, No.1, pp.155-163. https://doi.org/10.1109/TGRS.2009.2028613
  6. J. Nunez, X. Otazu, O. Fors, A. Prade, V. Pala, and R. Arbiol, 1999, "Multiresolution-based image fusion with additivie wavelet decomposition", IEEE Transactions on Geoscience and Remote Sensing, Vol.37, No.3, pp.1204-1211. https://doi.org/10.1109/36.763274
  7. J. Zhou, D. L. Civco, and J. A. Silander, 1998, "Awavelet transform method to merge Landsat TMand SPOT panchromatic data", InternationalJournal of Remote Sensing, Vol.19, No.4, pp.743-757. https://doi.org/10.1080/014311698215973
  8. L. Alparone, B. Aiazzi, S. Baronti, and A. Garzelli, 2003, "Sharpening of very high resolution imageswith spectral distortion minimization", Proc. IGARSS, pp.21-25.
  9. L. Alparone, S. Baronti, A. Garzelli, and F. Nencini, 2004, "A global quality measurement of pansharpened multispectral imagery", IEEE Transactions on Geoscience and Remote Sensing Letters, Vol.1, No.4, pp.313-317. https://doi.org/10.1109/LGRS.2004.836784
  10. L. Alparone, L. Wald, J. Chanusot, C. Thoma, O.Gamaba, and L. Mann Bruce, 2007, "Comparison ofpan-sharpening algorithms: outcome of the 2006GRS-S Data-Fusion Contest", IEEE Transactionson Geoscience and Remote Sensing, Vol.45,No.10, pp.3012-3021. https://doi.org/10.1109/TGRS.2007.904923
  11. M. Gonzalez-Audicana, X. Otazu, O. Fors and A.Seco, 2005, "Comparison between Mallat's and thea'trous discrete wavelet transformation basedalgorithms for the fusion of multispectral andpanchromatic images", International Journal ofRemote Sensing, Vol.26, No.3, pp.595-614. https://doi.org/10.1080/01431160512331314056
  12. M. Gonzalez-Audicana, X. Otazu, O. Fors, and J. A.Alvarex-Mozos, 2006, "A low computational-costmethod to fuse IKONOS images using the spectralresponse function of its sensors", IEEETransactions on Geoscience and Remote Sensing, Vol.44, No.6, pp.1683-1691. https://doi.org/10.1109/TGRS.2005.863299
  13. T. M. Tu, P. S. Huang, C. L. Hung, and C. P.Chang, 2004, "A fast intensity- hue-saturation fusiontechnique with spectral adjustment for IKONOSimagery", IEEE Transactions on Geoscience andRemote Sensing Letters, Vol.1, No.4, pp.309-312. https://doi.org/10.1109/LGRS.2004.834804
  14. W. Dou, Y. Chen, X. Li and D. Z. Sui, 2007, "Ageneral framework for component substitution imagefusion: An implementation using the fast imagefusion method", Computers & Geosciences, Vol.33, pp.219-228. https://doi.org/10.1016/j.cageo.2006.06.008
  15. X. Otazu, M. Gonzalez-Audicana, O. Fors, and J.Nunez, 2005, "Introdction of sensor spectralresponse into image fusion methods. Application towavelet-based methods", IEEE Transactions on Geoscience and Remote Sensing, Vol.43, No.10,pp.2376-2385. https://doi.org/10.1109/TGRS.2005.856106
  16. Y. Zhang, 2004, "Understanding image fusion", Photogrammetric Engineering & Remote Sensing, Vol.70, No.6, pp.653-660.