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Journal of Korean Society of Coastal and Ocean Engineers > Volume 32(6); 2020 > Article
J Korean Soc Coast Ocean Eng 2020;32(6): 553-560. doi: https://doi.org/10.9765/KSCOE.2020.32.6.553
드론과 HD 카메라를 이용한 수심측량시 잘피에 의한 오차제거 알고리즘
김경엽1, 최군환2, 안경모3
1한동대학교 공간설계공학과 대학원생
2한동대학교 부유식해상풍력발전연구소 연구소장
3한동대학교 공간환경시스템공학부 교수/부유식해상풍력발전연구소
Correction Algorithm of Errors by Seagrasses in Coastal Bathymetry Surveying Using Drone and HD Camera
Gyeongyeop Kim1, Gunhwan Choi2, Kyungmo Ahn3
1Graduate student, Department of Spatial Design & Engineering, Handong Global University
2Director of Institute, Research Institure of Floating Offshore Wind-power Generation Farm Field, Handong Global University
3Professor, School of Spatial Environment System Engineering, Handong Global University, Handong-ro, 558, Pohang, Kyeongbuk 37554, Korea
Corresponding author: Kyungmo Ahn ,Tel: +82-54-260-1421, Email: kmahn@handong.edu
Received: December 10, 2020;  Revised: December 21, 2020.  Accepted: December 22, 2020.
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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.
Keywords: drone, HD camera, seagrass, shallow water depth survey, error segmentation, L*a*b color space model
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