Journal of Korean Society of Coastal and Ocean Engineers (한국해안·해양공학회논문집)

Korean Society of Coastal and Ocean Engineers (한국해안·해양공학회)
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Correction Algorithm of Errors by Seagrasses in Coastal Bathymetry Surveying Using Drone and HD Camera

드론과 HD 카메라를 이용한 수심측량시 잘피에 의한 오차제거 알고리즘

  • Kim, Gyeongyeop (Department of Spatial Design & Engineering, Handong Global University) ;
  • Choi, Gunhwan (Research Institure of Floating Offshore Wind-power Generation Farm Field, Handong Global University) ;
  • Ahn, Kyungmo (School of Spatial Environment System Engineering, Handong Global University)
  • 김경엽 (한동대학교 공간설계공학과 대학원) ;
  • 최군환 (한동대학교 부유식해상풍력발전연구소) ;
  • 안경모 (한동대학교 공간환경시스템공학부/부유식해상풍력발전연구소)
  • Received : 2020.12.10
  • Accepted : 2020.12.22
  • Published : 2020.12.31
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Abstract

This paper presents an algorithm for identifying and eliminating errors by seagrasses in coastal bathymetry surveying using drone and HD camera. Survey errors due to seagrasses were identified, segmentated and eliminated using a L∗a∗b color space model. Bathymetry survey using a drone and HD camera has many advantages over conventional survey methods such as ship-board acoustic sounder or manual level survey which are time consuming and expensive. However, errors caused by sea bed reflectance due to seagrasses habitat hamper the development of new surveying tool. Seagrasses are the flowering plants which start to grow in November and flourish to maximum density until April in Korea. We developed a new algorithm for identifying seagrasses habitat locations and eliminating errors due to seagrasses to get the accurate depth survey data. We tested our algorithm at Wolpo beach. Bathymetry survey data which were obtained using a drone with HD camera and calibrated to eliminate errors due to seagrasses, were compared with depth survey data obtained using ship-board multi-beam acoustic sounder. The abnormal bathymetry data which are defined as the excess of 1.5 times of a standard deviation of random errors, are composed of 8.6% of the test site of area of 200 m by 300 m. By applying the developed algorithm, 92% of abnnormal bathymetry data were successfully eliminated and 33% of RMS errors were reduced.

드론 항공사진을 L∗a∗b 색공간으로 변환하고 항공사진에서 잘피가 나타난 영역을 분할 및 보정하여 드론 항공사진을 이용한 수심측량의 정확도를 향상시켰다. 드론을 이용한 수심측량은 음향측심기와 같은 보편적으로 통용되던 방식에 비해 저비용으로 빠른 시간에 수심자료를 얻을 수 있다. 그러나 수심측량 대상 해역에 잘피가 서식할 경우 해저면의 반사 특성이 일정하지 않아 드론을 이용한 수심측량시 오차가 발생한다. 우리나라에 서식하는 잘피를 비롯한 해조류는 수온이 낮아지기 시작하는 11월부터 자라기 시작하여 1~4월에 최대 밀도를 형성한다. 따라서 해당 시기의 드론 항공사진을 그대로 사용할 경우 수심측량의 정확도가 낮아지며, 이는 드론을 이용한 수심측량방식을 상용화하는데 극복해야 할 단점이다. 본 연구에서는 경북 월포해수욕장에서 드론으로 촬영한 고해상도 카메라 이미지를 분석하여 오차 발생해역을 구분하고 보정하는 알고리즘을 개발하였다. 또한, 보정한 드론 항공 사진으로 천해 수심 추정을 수행하여 알고리즘을 검증하였다. 잘피로 인한 오차 보정 알고리즘 적용 전 수심 5 m 이내의 200 m × 300 m 해역에서 발생하는 오차 표준편차의 1.5배를 넘는 오차 이상값 비율은 전체 이미지의 8.6%를 차지하였다. 오차 보정 알고리즘을 적용한 결과 오차 이상값의 92%가 제거되었으며, 평균제곱근오차(RMSE)는 33% 감소하였다.

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