Seabed sonar image analysis and acoustic seabed classification in the south of the Cheju Island

Tang Qiuhua; Li Jie; Zhou Xinghua; Lu Kai; Zhang Zhixun

doi:10.3969/j.issn.0253-4193.2014.04.015

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Article Navigation > Haiyang Xuebao > 2014 > 36(7): 133-141

Tang Qiuhua, Li Jie, Zhou Xinghua, Lu Kai, Zhang Zhixun. Seabed sonar image analysis and acoustic seabed classification in the south of the Cheju Island[J]. Haiyang Xuebao, 2014, 36(7): 133-141. doi: 10.3969/j.issn.0253-4193.2014.04.015

Citation:

Tang Qiuhua, Li Jie, Zhou Xinghua, Lu Kai, Zhang Zhixun. Seabed sonar image analysis and acoustic seabed classification in the south of the Cheju Island[J]. Haiyang Xuebao, 2014, 36(7): 133-141. doi: 10.3969/j.issn.0253-4193.2014.04.015

Citation:

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Seabed sonar image analysis and acoustic seabed classification in the south of the Cheju Island

doi: 10.3969/j.issn.0253-4193.2014.04.015

1.
Key Laboratory of Marine Hydrocarbon Resources and Environmental Geology, Ministry of Land and Resources, Qingdao 266071, China;The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China
2.
The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China
3.
Qingdao Institute of Marine Geology, Qingdao 266071, China

Received Date: 2013-04-23
Rev Recd Date: 2013-12-20

Abstract

Abstract

The selected area in this paper is located in the south of the Cheju Island. The study area is part of the Yellow Sea Trough extends to the Okinawa Trough and it's in the pathway of the Yellow Warm Current which is one of the branches of the Kuroshio Warm Current. Its seabed sonar image processing analysis and acoustic seabed classification research,contribute to a comprehensive understanding of the characteristics of the channel seabed bedforms surface texture and sediment distribution pattern. With the high-precision multibeam echo sounder sonar data in the research area,we apply image processing techniques and methods and get the seabed sonar image,then we can do quantitative description and analysis to the seabed surface texture features. Based on multibeam echo sounder backscatter strength data and 19 geological seabed sediment sample data,a statistical model which presents the relationship between seabed backscatter signal and sediment type is set up. Using improved Learning Vector Quantization neural network methods,a fast and accurate automatic identification for three seabed sediment types (TS,YS,STY) implementation is feasible.
- the south of the Cheju Island,
- multibeam echo sounder,
- seabed sonar image,
- acoustic seabed classification

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