Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Assessing Spatial Uncertainty Distributions in Classification of Remote Sensing Imagery using Spatial Statistics

공간 통계를 이용한 원격탐사 화상 분류의 공간적 불확실성 분포 추정

  • Park No-Wook (Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources) ;
  • Chi Kwang-Hoon (Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources) ;
  • Kwon Byung-Doo (Department of Earth Sciences, Seoul National University)
  • 박노욱 (한국지질자원연구원 지질자원정보센터) ;
  • 지광훈 (한국지질자원연구원 지질자원정보센터) ;
  • 권병두 (서울대학교 지구과학교육과)
  • Published : 2004.12.01
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Abstract

The application of spatial statistics to obtain the spatial uncertainty distributions in classification of remote sensing images is investigated in this paper. Two quantitative methods are presented for describing two kinds of uncertainty; one related to class assignment and the other related to the connection of reference samples. Three quantitative indices are addressed for the first category of uncertainty. Geostatistical simulation is applied both to integrate the exhaustive classification results with the sparse reference samples and to obtain the spatial uncertainty or accuracy distributions connected to those reference samples. To illustrate the proposed methods and to discuss the operational issues, the experiment was done on a multi-sensor remote sensing data set for supervised land-cover classification. As an experimental result, the two quantitative methods presented in this paper could provide additional information for interpreting and evaluating the classification results and more experiments should be carried out for verifying the presented methods.

이 논문은 원격탐사 화상 분류에서 공간적 불확실성 분포를 얻기 위해 공간 통계를 적용하였다. 분류 항목 할당과 참조 자료와 연계된 각각의 불확실성 표현을 위해 2가지 정량적 방법을 제안하였다. 우선 분류 항목 할당에 따른 불확실성 표현을 위해 3가지 정량적 지수를 제안하였다. 그리고 참조 자료와 분류 결과를 결합하고 이와 연계된 불확실성 혹은 정확성 분포를 얼기 위해 지구통계학적 시뮬레이션 기법을 적용하였다. 다중 센서 원격탐사 화상을 이용한 감독 토지 피복 분류 실험을 수행하여 제안 방법을 예시하고, 적용시 제안점을 논의하였다. 실험 결과, 이 논문에서 제시한 방법론을 통해 분류 결과의 해석과 평가를 위한 부가적인 정보추출이 가능하였으며, 제시 방법론의 검증을 위한 다양한 자료에의 적용이 필요한 것으로 판단된다.

Keywords

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