• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.55 no.1
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• pp.39-49
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• 2019

Acoustics are increasingly regarded as a remote-sensing tool that provides the basis for classifying and mapping ocean resources including seabed classification. It has long been understood that details about the character of the seabed (roughness, sediment type, grain-size distribution, porosity, and material density) are embedded in the acoustical echoes from the seabed. This study developed a sophisticated yet easy-to-use technique to discriminate seabed characteristics using a split beam echosounder. Acoustic survey was conducted in Tongyeong waters, South Korea in June 2018, and the verification of acoustic seabed classification was made by the Van Veen grab sampler. The acoustic scattering signals extracted the seabed hardness and roughness components as well as various seabed features. The seabed features were selected using the principal component analysis, and the seabed classification was performed by the K-means clustering. As a result, three seabed types such as sand, mud, and shell were discriminated. This preliminary study presented feasible application of a sounder to classify the seabed substrates. It can be further developed for characterizing marine habitats on a variety of spatial scales and studying the ecological characteristic of fishes near the habitats.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.35 no.3
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• pp.273-281
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• 2002

Determination of sediment type is generally based on ground truthing. This method, however, provides information only for the limited sites. Recent developments of remote classification of seafloor sediments made it possible to obtain continuous profiles of sediment types. QTC View system, which is an acoustic instrument providing digital real-time seabed classification, was used to classify seafloor sediment types in the Suyoung Bay, Pusan. QTC View was connected to 50 kHz echo sounder, All parameters of QTC View and echo sounder are uniformly kept during survey. By ground truthing, the sediments are classified into seven types, such as slightly gravelly sand, slightly gravelly sandy mud, gravelly muddy sand, clayey sand, sandy mud, slightly gravelly muddy sand, and rocky bottom. By the first remote classification using QTC View, four sediment types are clearly identified, such as slightly gravelly sand, gravelly mud, slightly gravelly muddy sand, and rocky bottom. These are similar to the result of the second survey. Also the result of remote classification matches well with that of ground truthing, but for sediment type determined by minor component. Therefore, QTC View can effectively be used for remote classification of seafloor sediments.

• Economic and Environmental Geology
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• v.41 no.6
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• pp.747-761
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• 2008

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$(\phi)$ to 0.88(\phi). 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.