Econ. Environ. Geol. 2008; 41(6): 747-761

Published online December 31, 2008

© THE KOREAN SOCIETY OF ECONOMIC AND ENVIRONMENTAL GEOLOGY

Surficial Sediment Classification using Backscattered Amplitude Imagery of Multibeam Echo Sounder(300 kHz)

Yosup Park1*, Sinje Lee1, WonJin Seo1, Gee Soo Gong2, HyukSoo Han3 and SooChul Park3

1UST21, Yongun B/D 5F, Sungui dong, NamGu, Inchon, 402-011, Korea
2Petroleum & Marine Resources Research division, Korea Institute of Geoscience and Mineral Resources, Daejeon 305-350, Korea
3Department of Oceanography, Chungnam National University, 305-764, Korea

Correspondence to :

Yosup Park

joseph@ust21.co.kr

Received: June 14, 2008; Accepted: December 24, 2008

Abstract

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.

Keywords multibeam, backscatter, GIS, grain size, classification

Article

Econ. Environ. Geol. 2008; 41(6): 747-761

Published online December 31, 2008

Copyright © THE KOREAN SOCIETY OF ECONOMIC AND ENVIRONMENTAL GEOLOGY.

Surficial Sediment Classification using Backscattered Amplitude Imagery of Multibeam Echo Sounder(300 kHz)

Yosup Park1*, Sinje Lee1, WonJin Seo1, Gee Soo Gong2, HyukSoo Han3 and SooChul Park3

1UST21, Yongun B/D 5F, Sungui dong, NamGu, Inchon, 402-011, Korea
2Petroleum & Marine Resources Research division, Korea Institute of Geoscience and Mineral Resources, Daejeon 305-350, Korea
3Department of Oceanography, Chungnam National University, 305-764, Korea

Correspondence to:

Yosup Park

joseph@ust21.co.kr

Received: June 14, 2008; Accepted: December 24, 2008

Abstract

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.

Keywords multibeam, backscatter, GIS, grain size, classification

    KSEEG
    Apr 30, 2024 Vol.57 No.2, pp. 107~280

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