다중빔 음향 탐사 시스템(300 kHz)의 후방산란 자료를 이용한 해저면 퇴적상 분 류에 관한 연구 (Surficial Sediment Classification using Backscattered Amplitude Imagery of Multibeam Echo Sounder(300 kHz))

서지정보

· 발행기관 : 대한자원환경지질학회
· 수록지 정보 : 자원환경지질 / 41권 / 6호 / 747 ~ 761페이지
· 저자명 : 박요섭, 이신제, 서원진, 공기수, 한혁수, 박수철

초록

In order to experiment the acoustic remote classification of seabed sediment, we achieved ground-truth data(i.e. video and grab samples, etc.) and developed post-processing for automatic classification procedure on the basis of 300 kHz MultiBeam Echo Sounder(MBES) backscattering data, which was acquired using KONGBERG Simrad EM3000 at Sock-Cho Port, East Sea of South Korea. Sonar signal and its classification performance were identified
with geo-referenced video imagery with the aid of GIS(Geographic Information System). The depth range of research site was from 5 m to 22.7 m, and the backscattering amplitude showed from -36dB to -15dB. The mean
grain sizes of sediment from equi-distanced sampling site(50 m interval) varied from 2.86(φ ) to 0.88(φ ). To acquire the main feature for the seabed classification from backscattering amplitude of MBES, we evaluated the correlation factors between the backscattering amplitude and properties of sediment samples.The performance of seabed remote classification proposed was evaluated with comparing the correlation of human expert segmentation to automatic algorithm results. The cross-model perception error ratio on automatic classification algorithm shows 8.95% at rocky bottoms, and 2.06% at the area representing low mean grain size.

영어초록

In order to experiment the acoustic remote classification of seabed sediment, we achieved ground-truth data(i.e. video and grab samples, etc.) and developed post-processing for automatic classification procedure on the basis of 300 kHz MultiBeam Echo Sounder(MBES) backscattering data, which was acquired using KONGBERG Simrad EM3000 at Sock-Cho Port, East Sea of South Korea. Sonar signal and its classification performance were identified
with geo-referenced video imagery with the aid of GIS(Geographic Information System). The depth range of research site was from 5 m to 22.7 m, and the backscattering amplitude showed from -36dB to -15dB. The mean
grain sizes of sediment from equi-distanced sampling site(50 m interval) varied from 2.86(φ ) to 0.88(φ ). To acquire the main feature for the seabed classification from backscattering amplitude of MBES, we evaluated the correlation factors between the backscattering amplitude and properties of sediment samples.The performance of seabed remote classification proposed was evaluated with comparing the correlation of human expert segmentation to automatic algorithm results. The cross-model perception error ratio on automatic classification algorithm shows 8.95% at rocky bottoms, and 2.06% at the area representing low mean grain size.

참고자료

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