Journal of the Korea Academia-Industrial cooperation Society (한국산학기술학회논문지)

The Korea Academia-Industrial cooperation Society (한국산학기술학회)
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Classification of Terrestrial LiDAR Data through a Technique of Combining Heterogeneous Data

이기종 측량자료의 융합기법을 통한 지상 라이다 자료의 분류

  • 김동문 (남서울대학교 GIS공학과) ;
  • 김성훈 (남서울대학교 GIS공학과)
  • Received : 2011.07.29
  • Accepted : 2011.09.08
  • Published : 2011.09.30
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Abstract

Terrestrial LiDAR is a high precision positioning technique to monitor the behavior and change of structures and natural slopes, but it has depended on subjective hand intensive tasks for the classification(surface and vegetation or structure and vegetation) of positioning data. Thus it has a couple of problems including lower reliability of data classification and longer operation hours due to the surface characteristics of various geographical and natural features. In order to solve those problems, the investigator developed a technique of using the NDVI, which is a major index to monitor the changes on the surface(including vegetation), to categorize land covers, combining the results with the terrestrial LiDAR data, and classifying the results according to items. The application results of the developed technique show that the accuracy of convergence was 94% even though there was a problem with partial misclassification of 0.003% along the boundaries between items. The technique took less time for data processing than the old hand intensive task and improved in accuracy, thus increasing its utilization across a range of fields.

지상라이다는 구조물과 자연사면의 거동이나 변화를 모니터링 할 수 있는 고정밀 측위기술이지만 측위자료를 대상으로 한 분류작업(지표면과 식생 또는 구조물과 식생)은 주관적인 수작업에 의존하게 된다. 그 결과 다양한 지형지물이 혼재해 있는 지표특성으로 인해 자료분류의 신뢰도는 떨어지며, 작업시간은 길어지는 문제가 있다. 이러한 문제를 해결하기 위해 지표면(식생 등)의 변화탐지 모니터링을 위한 주요한 지표로 사용되는 NDVI(Normalized Difference Vegetation Index)를 이용하여 피복을 분류하고 그 결과를 지상라이다 자료와 융합하여 항목별로 분류하는 기법을 개발하였다. 개발기법을 적용한 결과, NDVI 자료는 항목 간 경계지점에서 0.003%의 오(誤) 분류가 있었으나 약 94%의 융합 정확도를 나타내었고 기존의 수작업에 비해 자료처리 시간이 짧아지며 정확도가 높아져 다양한 분야에 활용도가 높아질 것으로 판단된다.

Keywords

References

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