대한원격탐사학회지 (Korean Journal of Remote Sensing)

  • 제30권2호
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  • Pages.219-226
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  • 2014
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  • 1225-6161(pISSN)
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  • 2287-9307(eISSN)
대한원격탐사학회 (Korean Society of Remote Sensing)
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수정된 IEA 기반의 분광혼합분석 기법을 이용한 임상분류

Spectral Mixture Analysis Using Modified IEA Algorithm for Forest Classification

  • 송아람 (서울대학교 공과대학 건설환경공학부) ;
  • 한유경 (서울대학교 공과대학 건설환경공학부) ;
  • 김용현 (서울대학교 공과대학 건설환경공학부) ;
  • 김용일 (서울대학교 공과대학 건설환경공학부)
  • Song, Ahram (Department of Civil and Environmental Engineering, Seoul National University) ;
  • Han, Youkyung (Department of Civil and Environmental Engineering, Seoul National University) ;
  • Kim, Younghyun (Department of Civil and Environmental Engineering, Seoul National University) ;
  • Kim, Yongil (Department of Civil and Environmental Engineering, Seoul National University)
  • 투고 : 2014.02.14
  • 심사 : 2014.03.21
  • 발행 : 2014.04.30
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초록

분광혼합분석 결과로 얻어지는 각 물체의 점유비율을 활용하면 보다 세밀한 분류가 가능하다. 이는 복잡한 도심지역의 피복분류 뿐만 아니라 혼효림이 많은 한반도 임상분류에 적합한 분류기법이 될 수 있다. 효과적인 임상분류를 위해서는 무엇보다 적절한 endmember의 추출이 선행되어야 하는데, 기존에 주로 사용되었던 기하학적 방법(geometric endmember selection)은 분광특성이 유사한 산림지역에 적합하지 않다. 본 연구에서는 영상에서 직접 순수한 화소를 추출하는 기법 중의 하나인 IEA(Iterative Error Analysis)와 침엽수와 활엽수의 분광특성을 이용하여 실험지역을 대표할 수 있는 각각의 endmember를 자동으로 추출하였다. CASI(Compact Airborne Spectrographic Imager) 영상의 두 지역에 대하여 분광혼합분석을 이용한 분류를 수행한 결과, 분류 정확도는 각각 86%와 90%로, 제안한 기법이 실험대상지역을 대표하는 침엽수와 활엽수의 endmember를 적절하게 추출한 것으로 나타났다. 분광혼합분석 기법을 이용한 보다 효과적인 분류를 위해서 분류항목 외 기타물질을 endmember로 고려하는 연구가 필요할 것으로 보인다.

Fractional values resulted from the spectral mixture analysis could be used to classify not only urban area with various materials but also forest area in more detailed spatial scale. Especially South Korea is largely consist of mixed forest, so the spectral mixture analysis is suitable as a classification method. For the successful classification using spectral mixture analysis, extraction of optimal endmembers is prerequisite process. Though geometric endmember selection has been widely used, it is barely suitable for forest area. Therefore, in this study, we modified Iterative Error Analysis (IEA), one of the most famous algorithms of image endmember selection which extracts pure pixel directly from the image. The endmembers which represent deciduous and coniferous trees are automatically extracted. The experiments were implemented on two sites of Compact Airborne Spectrographic Imager (CASI) and classified forest area into two types. Accuracies of each classification results were 86% and 90%, which mean proposed algorithm effectively extracted proper endmembers. For the more accurate classification, another substances like forest gap should be considered.

키워드

참고문헌

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피인용 문헌

  1. Urbanization and Quality of Stormwater Runoff: Remote Sensing Measurements of Land Cover in an Arid City vol.30, pp.3, 2014, https://doi.org/10.7780/kjrs.2014.30.3.6
  2. Iterative Error Analysis 기반 분광혼합분석에 의한 초분광 영상의 표적물질 탐지 기법 vol.33, pp.5, 2014, https://doi.org/10.7780/kjrs.2017.33.5.1.8