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Exploring the Biosynthetic Potential of Microorganisms from the South China Sea Cold Seep Using Culture-Dependent and Culture-Independent Approaches. [PDF]

Mar Drugs
Hu GA, Sun HY, Yin QJ, Wang H, Liu SY, Wang BG, Wang H, Li X, Wei B.   +8 more
europepmc   +1 more source

Analysis of Dynamic Changes in Sedimentation in the Coastal Area of Amir-Abad Port Using High-Resolution Satellite Images. [PDF]

J Imaging
Sam-Khaniani A, Viccione G, Qorbani Fouladi M, Hesabi-Fard R.   +3 more
europepmc   +1 more source

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Seabed Sub-Bottom Sediment Classification Using Artificial Intelligence

Journal of Coastal Research, 2021
Kim, H.D.; Aoki, S.; Oh, H.; Kim, K.H.; and Oh, J., 2021. Seabed sub-bottom sediment classification using artificial intelligence. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 305–309.
Hyun Dong Kim, Shin-ich Aoki, Hyumin Oh, Kyu Han Kim, Juhye Oh   +4 more
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A novel visual classification method of seabed sediments

2014 Oceans - St. John's, 2014
This study aims at the autonomous seafloor surveillance by underwater vehicles based on computer vision techniques. A novel scheme of seabed image classification is proposed to identify three types of seabed sediments. The texture features of seabed sediments were described by using gray-level co-occurrence matrix and fractal dimension.
Yan Li, Chunlei Xia, Yan Huang, Puqiang Zhu, Liya Ge   +4 more
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The Discussion of Acoustic Seabed Sediment Classification Methods

Applied Mechanics and Materials, 2012
Acoustic seabed sediment classification method is always important research contents in marine geology and marine acoustics because of its characters of low-cost and high efficiency. At present, there are mainly three types of acoustic seabed sediment classification methods:(1) the echo signal statistical characteristics classification; (2) image ...
Hong Bo Zheng, Pin Yan, Jing Chen
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Seabed Sediment Classification based on Multi-features Fusion and Feature Selection Framework

2021 OES China Ocean Acoustics (COA), 2021
An improved framework used to classify sediment using the signal reflected by the seafloor is put forward in this paper. The proposed framework is verified by the data collected by 400-kHz Reson 7125 multibeam echo sounder(MBES) in the experiment carried out by California Seabed Mapping Project.
Pang Yan, Xu Feng, Liu Jia-Zhao Yue
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Automatic classification of seabed sediments based on HLAC

Proceedings of the 2013 IEEE/SICE International Symposium on System Integration, 2013
Understanding the distribution of seafloor sediment using a side-scan sonar is very important to grasp the distribution of seabed resources. This task is traditionally carried out by a skilled human operator. However, with the appearance of Autonomous Underwater Vehicles, automated processing is now needed to tackle the large amount of data produced ...
Yasuhiro Tan, Joo Kooi Tan, Hyoungseop Kim, Seiji Ishikawa   +3 more
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Study on offshore seabed sediment classification based on particle size parameters using XGBoost algorithm

Computers & Geosciences, 2021
Abstract Folk's textual classification scheme which is widely used for sediment study operates with the proportions of gravel, sand, silt and clay fractions conventionally. However, dealing with data from different sources usually needs to face missing values that may make the classification difficult. To solve this problem and discover other methods
Fengfan Wang, Jia Yu, Zhijie Liu, Min Kong, Yunfan Wu   +4 more
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Deep learning model for seabed sediment classification based on fuzzy ranking feature optimization

Marine Geology, 2020
Abstract Accurate acquisition of information on seabed sediment distributions plays an important role in the construction of basic marine geographic databases. Although a multibeam echo-sounding system (MBES) can satisfy large-scale seafloor mapping with high precision and high resolution, the development of a consistent, stable, repeatable and ...
Xiaodong Cui, Fanlin Yang, Fanlin Yang, Xin Wang, Bo Ai, Yu Luo, Dan Ma   +6 more
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Visual Features Extraction and Types Classification of Seabed Sediments

2014
The purpose of this research is to define and extract the visual features of the seabed sediments to improve the autonomous ability of a underwater vehicle while implementing exploring missions. A scheme of seabed image classification is proposed to identify three types of seabed sediments.
Yan Li, Chunlei Xia, Yan Huang, Liya Ge, Yu Tian   +4 more
openaire   +1 more source