DOI QR코드

DOI QR Code

Hyperspectral Image Fusion Algorithm Based on Two-Stage Spectral Unmixing Method

2단계 분광혼합기법 기반의 하이퍼스펙트럴 영상융합 알고리즘

  • Choi, Jae-Wan (School of Civil, Urban & Geosystem Engineering, Seoul National University) ;
  • Kim, Dae-Sung (School of Civil, Urban & Geosystem Engineering, Seoul National University) ;
  • Lee, Byoung-Kil (Civil Engineering, Hankong National University) ;
  • Yu, Ki-Yun (School of Civil, Urban & Geosystem Engineering, Seoul National University) ;
  • Kim, Yong-Il (School of Civil, Urban & Geosystem Engineering, Seoul National University)
  • 최재완 (서울대학교 공과대학 지구환경시스템공학부) ;
  • 김대성 (서울대학교 공과대학 지구환경시스템공학부) ;
  • 이병길 (한경대학교 토목공학과) ;
  • 유기윤 (서울대학교 공과대학 지구환경시스템공학부) ;
  • 김용일 (서울대학교 공과대학 지구환경시스템공학부)
  • Published : 2006.08.01

Abstract

Image fusion is defined as making new image by merging two or more images using special algorithms. In case of remote sensing, it means fusing multispectral low-resolution remotely sensed image with panchromatic high-resolution image. Generally, hyperspectral image fusion is accomplished by utilizing fusion technique of multispectral imagery or spectral unmixing model. But, the former may distort spectral information and the latter needs endmember data or additional data, and has a problem with not preserving spatial information well. This study proposes a new algorithm based on two stage spectral unmixing model for preserving hyperspectral image's spectral information. The proposed fusion technique is implemented and tested using Hyperion and ALI images. it is shown to work well on maintaining more spatial/spectral information than the PCA/GS fusion algorithms.

영상융합은 "특정 알고리즘의 사용을 통해 두 개 혹은 그 이상의 서로 다른 영상을 조합하여 새로운 영상을 만들어내는 것"을 뜻하며 원격탐사에서는 주로 낮은 공간해상도의 멀티스펙트럴 영상과 높은 공간해상도의 흑백영상을 융합하여 높은 공간해상도의 멀티스펙트럴 영상을 생성하는 것을 의미한다. 일반적으로 하이퍼스펙트럴 영상융합을 위해서는 기존의 멀티스펙트럴 영상융합 기법을 이용한 방법이나 분광혼합기법을 이용한 방법을 사용한다. 전자의 경우에는 분광정보가 손실될 가능성이 높으며, 후자의 경우는, endmember의 정보나 부가적인 데이터가 필요하고 결과 영상의 경우 공간적 정보가 상대적으로 부정확한 문제점을 보인다. 따라서 본 연구에서는 하이퍼스펙트럴 영상의 분광특성을 보존하기 위한 융합방법으로서 2단계 분광혼합기법을 사용한 영상융합 알고리즘을 제안하였으며 이를 실제 Hyperion, ALI 영상에 적용하여 평가하였다. 이를 통해 제안한 알고리즘에 의해서 융합된 결과가 PCA, GS 융합기법에 비해서 높은 공간, 분광 해상도를 유지할 수 있음을 보여주었다.

Keywords

References

  1. Ali Darvishi, 2005. Hyper-spectral/High-Resolution Data fusion: Assessing the Quality of EO1-Hyperion/Spot-Pan & Quickbird-MS fused images in Spectral Domain, ISPRS Hannover Workshop
  2. Antonio Plaza, Pablo Martinez, Rosa Perez, and Javier Plaza, 2004. A Quantitative and Comparative Analysis of Endmember Extraction Algorithms From Hyperspectral Data, IEEE Transaction on Geoscience and Remote Sensing, 42(3): 650-663 https://doi.org/10.1109/TGRS.2003.820314
  3. Chein-I Chang and Daniel C. Heinz, 2000. Constrained Subpixel Target Detection for Remotely Sensed Imagery, IEEE Transactions on Geoscience and Remote Sensing, 38(3): 1144-1159 https://doi.org/10.1109/36.843007
  4. Daniel C. Heinz and Chein-I, 2001. Fully Constrained Least Squares Linear Spectral Mixture Analysis Mehtod for Material Quantification in Hyperspectral Imagery, IEEE Transactions on Geoscience and Remote Sensing, 39(3): 529-545 https://doi.org/10.1109/36.911111
  5. Gary D. Robinson, Harry N. Gross, and John R. Schott, 2000. Evaluation of Two Applications of Spectral Mixing Models to Image Fusion, Remote Sensing of Environment, 71: 272-281 https://doi.org/10.1016/S0034-4257(99)00074-7
  6. Harvey E. Rhody, 2002. Enhancing Spatial Resolution for Exploitation in Hyperspectral Imagery, IEEE Proceedings of 31st Applied Imagery Pattern Recognition Workshop (AIPR 02)
  7. Michael T. Eismann and Russell C. Hardie, 2005. Hyperspectral Resolution Enhancement Using High-Resolution Multispectral Imagery With Arbitrary Response Functions, IEEE Transactions on Geoscience and Remote Sensing, 43(3): 455-465 https://doi.org/10.1109/TGRS.2004.837324
  8. Rasmus Bro and Sijmen De Jong, 1997. A fast non - negativity-constrained least squares algorithm, Journal of Chemometrics, 11: 393-401 https://doi.org/10.1002/(SICI)1099-128X(199709/10)11:5<393::AID-CEM483>3.0.CO;2-L
  9. Russell C. Hardie and Michael T. Eismann, 2004. MAP Estimation for Hyperspectral Image Resolution Enhancement Using an Auxiliary Sensor, IEEE Transactions on Geoscience and Remote Sensing, 13(9): 1174-1184
  10. Roger L. Schultz and Martin T. Hagan, 1999. On-line Least-Squares Training For The Underdetermined Case, International Joint Conference on Neural Networks, July, Washington, Paper No. 515
  11. Soo Chin Liew, Chew Wai Chang, and Leong Keong Kwoh, 2003. Image Fusion of Hyperion and IKONOS imagery, Image Processing and Pattern Recognition in Remote Sensing, Proceedings of SPIE, 4898: 31-35
  12. Yun Zhang, 2004. Understanding Image Fusion, PE & RS, June: 657-661
  13. Zhizun Wang, Djemel Ziou, Costas Armenakis, Deren Li, and Qingquan 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
  14. Zhou Wang and Alan C. Bovik, 2002. A Universal Image Quality Index, IEEE Signal Processing Letters, XX(Y), March: 1-4