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http://dx.doi.org/10.9765/KSCOE.2020.32.6.553

Correction Algorithm of Errors by Seagrasses in Coastal Bathymetry Surveying Using Drone and HD Camera  

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)
Publication Information
Journal of Korean Society of Coastal and Ocean Engineers / v.32, no.6, 2020 , pp. 553-560 More about this Journal
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;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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