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Application of Linear Spectral Mixture Analysis to Geological Thematic Mapping using LANDSAT 7 ETM+ and ASTER Satellite Imageries

LANDSAT 7 ETM+와 ASTER영상정보를 이용한 선형분광혼합분석 기법의 지질주제도 작성 응용

  • Kim Seung Tae (Korea Resources Corporation, Graduate School, Hansung University) ;
  • Lee Kiwon (Dept. of Information Systems Engineering, Hansung University)
  • 김승태 (대한광업진흥공사, 한성대학교 대학원 공간정보분석학과) ;
  • 이기원 (한성대학교 정보시스템공학과)
  • Published : 2004.12.01

Abstract

The purpose of this study is the investigation of applicability of LSMA(Linear Spectral Mixture Analysis) on the geological uses with different radiometric and spatial types of sensor images such as Terra ASTER and LANDSAT 7 ETM+. As for the actual application case, geologic mapping for mineral exploration using ASTER and ETM+ at the Mongolian plateau region was carried out. After the pre-processing such as the geometric corrections and calibration of radiance, 7 endmembers, as spectral classes for geologic rock types, related to spectral signature deviation for the given application was determined by the pre-surveyed geological mapping information and the correlation matrix analysis, and total 20 images of ASTER and ETM+ were used to LSMA processing. As the results, fraction maps showing individual mineral types in the study area are presented. It concluded that this approach based on LSMA using ETM+ and ASTER is regarded as one of the effective schemes for geologic remote sensing.

본 연구는 Terra ASTER 영상과 LANDSAT 7 ETM+ 분광 영상정보와 같은 상이한 방사 및 공간 해상도를 갖는 위성 센서의 영상을 지질학적으로 활용하기 위한 선형분광혼합분석(LSMA: Linear Spectral Mixture Analysis)기법의 적용성을 목적으로 한다 실제 적용사례로서 몽골지역을 대상으로 ASTER 영상과 LANDSAT 7 ETM+ 분광 영상정보를 이용하여 지질학적 주제도 자성과정을 수행하였다. 두 영상 정보에 대하여 기하 보정 및 방사 휘도 조정 등의 전처리 작업을 수행한 후 사전 지질조사 정보와 두 영상정보의 밴드 별 상관도를 분석하여 7개의 지질단위의 분광 클래스를 선택하였고 20개 밴드완 위성 영상자료를 LSMA 기법에 적용하였다. 처리 결과로 주제도 작성의 대상으로 한 7개의 지질단위에 대한 각각의 주제도를 얻게 되었다. 결론적으로 LSMA 기법은 지질 주제도 작성을 위한 효과적인 접근 방법 중의 하나로 판단된다.

Keywords

References

  1. 이지민, 이규성, 2003. 분광혼합분석 기법에 의한 산림피복 정보의 특성 분석, 대한원격탐사학회지, 19: 411-420
  2. Abrams, M. and S. Hook, 2000. ASTER user handbook, version 2, JPL, 135p
  3. Adams, J. B., M. O. Smith, and P. E. Johnston, 1986. Spectral Mixture Modeilong: A New Analysis of Rock and Soil Types at the Viking Lander I Site, Journal of Geophysical Research, 91:8098-8112
  4. Bedell, R., 2004. Remote Sensing in Mineral Exploration, SEG Newsletter, No.58
  5. Buheaosier, K., M. Tsuchiya, and S. J. Kaneko, 2003. Comparison of Image Data Acquired with AVHRR, MODIS, ETM+ and ASTER over HOKKAIDO, JAPAN, Adv. Space Res., 32: 2211-2216 https://doi.org/10.1016/S0273-1177(03)90544-8
  6. Dennison, P. E., and D. A. Roberts, 2003. Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE, Remote Sensing of Environment, 87: 123-135
  7. ENVI, 2001, ENVI Tutorials: Multispectral Processing using ENVI's Hyperspectral Tools, pp.449-481
  8. Feng, J., B. Rivald, and A. S. Azofeifa, 2003. The topographic normalization of hyperspectral data: implication for the selection of spectral end members and lithologic mapping, Remote Sensing of Environment, 85: 221-231
  9. Huguenin,R. L, M. A. Karaska, D. V. Blaricom, and J. R. Jensen, 1997. Subpixel classification of Bald Cypress and Tupelo Gum Trees in Thematic Mapper Imagery, Photogrammetric Engineering & Remote Sensing, 63: 717-725
  10. Kim, S-W and C-H Park, 2004. Linear Spectral Mixture Analysis of Landsat Imagery for Wetland Land-Cover Classfication in Paldang Reservoir and Vicinity, Korean Journal of Remote Sensing, 20: 197-205
  11. Lu, D., M. Batistella, and E. Moran, 2002. Linear Spectral Mixture Analysis of TM Data for Land-use and Land-cover Classification in Rondonia, Brazilian Amazon, Symp. On Geospatial Theory,Processing and Applications, Ottawa
  12. Neteler, M., 1999. Spectral Mixture Analysis and Atmosphere/Terrain Correction of LANDSAT Images for Erosion Modelling Using GRASS, Presentation at lTC, IRST, Trento, 20p
  13. Newland, D., 1999. Evaluation of Stepwise Spectral Unmixing with HYDICE Data, Available at:http://www.cis.rit.edu/research/thesis/bs/1999 /newland/thesis.html
  14. Schweik, C. M. and G. M. Green, 1999. The Use of Spectral Mixture Analysis to Study Human Incentives, Actions, and Environmental Outcomes, Social Science Computer Review, 17:40-63
  15. Small, C., 2003. High spatial resolution spectral mixture analysis of urban reflectance, Remote Sensing of Environment, 88: 170-186
  16. Vikhamar, D. and R. Solberg, 2003. Snow-cover mapping in forests by constrained linear spectral unmixing of MODIS data, Remote Sensing of Environment, 88: 309-323
  17. Wu, C., 2004. Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery, Remote Sensing of Environment,93: 480-492