한국수자원학회논문집 (Journal of Korea Water Resources Association)

  • 제54권12호
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  • Pages.1265-1274
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  • 2021
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  • 2799-8746(pISSN)
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  • 2799-8754(eISSN)
한국수자원학회 (Korea Water Resources Association)
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현장 및 원격 초분광 정보 계측을 통한 하천 수변공간 재료 구분

Field and remote acquisition of hyperspectral information for classification of riverside area materials

  • 신재현 (한국건설기술연구원 수자원하천연구본부) ;
  • 성호제 (한국건설기술연구원 기획조정본부) ;
  • 이동섭 (한국건설기술연구원 수자원하천연구본부)
  • Shin, Jaehyun (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Seong, Hoje (Planning and Coordination Department, Korea Institute of Civil Engineering and Building Technology) ;
  • Rhee, Dong Sop (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology)
  • 투고 : 2021.08.30
  • 심사 : 2021.11.02
  • 발행 : 2021.12.31
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초록

본 연구에서는 남한강에서 드론에 탑재된 초분광 센서를 활용하여 수변공간을 측정한 후, 초분광 분석을 통하여 재료를 구분하였다. 식생, 콘크리트, 흙 등의 재료를 대상으로 구분하였으며, 각각 재료의 고유한 분광반사 곡선의 특성을 비교 및 분석하였다. 드론으로 측정한 초분광 자료를 검증하기 위하여 지상분광측정기를 사용하여 현장조사를 실시하고 각 재료를 비교하였다. 분석 비교 결과 각 재료별로 고유한 유형의 파장대가 발생하는 것을 확인하였고 드론으로 수행한 원격 탐사 결과가 지상분광측정 결과와 유사하다는 결론을 내릴 수 있었다. 수변 공간의 분류를 위하여 K-means 군집화 기법과 SVM 분류 기법을 활용하여 측정 구역의 공간 분류를 수행할 수 있었다. 비교 결과, 지도학습인 SVM 분류 기법의 수변공간 분류가 비지도학습인 K-means 기법과 비교하여 상세한 구분이 수행되었음을 확인할 수 있었다. 이와 같이 분류 및 군집 분석 기법을 활용하여 각 수변공간 재료의 고유 분광 특성을 활용하여 측정되는 드론탑재 초분광 이미지의 각 데이터를 분류할 수 있게 되었다.

The analysis of hyperspectral characteristics of materials near the South Han River has been conducted using riverside area measurements by drone installed hyperspectral sensors. Each spectrum reflectance of the riverside materials were compared and analyzed which were consisted of grass, concrete, soil, etc. To verify the drone installed hyperspectral measurements, a ground spectrometer was deployed for field measurements and comparisons for the materials. The comparison results showed that the riverside materials had their unique hyperspectral band characteristics, and the field measurements were similar to the remote sensing data. For the classification of the riverside area, the K-means clustering method and SVM classification method were utilized. The supervised SVM method showed accurate classification of the riverside area than the unsupervised K-means method. Using classification and clustering methods, the inherent spectral characteristic for each material was found to classify the riverside materials of hyperspectral images from drones.

키워드

과제정보

본 연구는 국토교통부/국토교통과학기술진흥원의 지원으로 수행되었습니다(과제번호 21DPIW -C153746-03).

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