Results 221 to 230 of about 23,689 (244)
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Efficient seafloor classification and cable route design using an AUV
OCEANS 2015 - Genova, 2015This paper aims to an efficient method for submarine cable route design using online seafloor classification from sonar scanlines conducted by an autonomous underwater vehicle (AUV). Currently, the cable route design works are carried out by experienced surveyors and engineers by hand. An online seafloor classification using an AUV with automated route
Sheng-wei Huang +2 more
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Neural nets for seafloor classification: A computer simulation study
The Journal of the Acoustical Society of America, 1990The point scattering model of reverberation offers estimates of a large number of reverberation statistics. However, fitting the model to data is not immediately meaningful owing to the model's weak physical connection. The approach taken here utilizes parametrizations of the reverberation probability density function (pdf) to create acoustic “feature ...
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The detection and classification of objects lying on the seafloor
The Journal of the Acoustical Society of America, 1988There are many instances when the detection of an object lying on the seafloor is insufficient. In many cases, an idea of what kind of object is lying on the seafloor is also required. The detection and classification sonar described here is based on a wide bandwidth continuous transmission frequency-modulated (CTFM) sonar.
A. de Roos +4 more
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Seafloor scattering: Modeling and analysis of classification clues
The Journal of the Acoustical Society of America, 1999Possibilities of scattering data inversion for various seabed properties are analyzed using recently developed models of acoustic bottom interaction. Different scattering mechanisms are considered giving the main contributions to total bottom scattering which are due to different types of medium irregularities: volume inhomogeneities of the sediment ...
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Seafloor Classification of Multibeam Sonar Data Using Neural Network Approach
Marine Geodesy, 2005In this study, the self-organizing map (SOM), which is an unsupervised clustering algorithm, and a supervised proportional learning vector quantization (PLVQ), are employed to develop a combined method of seafloor classification using multibeam sonar backscatter data.
Xinghua Zhou, Yongqi Chen
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Computers & Geosciences, 1996
Abstract A geostatistical method for automated seafloor classification is developed and applied to bathymetrie data for a 150 × 100 km area at 26 °N on the western flank of the Mid-Atlantic Ridge. The objective of seafloor classification is to characterize seafloor properties quantitatively, and to use such spatial characteristics to distinguish ...
Ute Christina Herzfeld +1 more
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Abstract A geostatistical method for automated seafloor classification is developed and applied to bathymetrie data for a 150 × 100 km area at 26 °N on the western flank of the Mid-Atlantic Ridge. The objective of seafloor classification is to characterize seafloor properties quantitatively, and to use such spatial characteristics to distinguish ...
Ute Christina Herzfeld +1 more
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Research Progress of Seafloor Pockmarks in Spatio-Temporal Distribution and Classification
Journal of Ocean University of China, 2019Seafloor pockmarks are important indicators of submarine methane seepages and slope instabilities. In order to promote the understanding of submarine pockmarks and their relationship with sediment instabilities and climate changes, here we summarize the research results of pockmarks in the spatio-temporal distributions and shaping factors.
Cuiling Xu +4 more
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OCEANS 2006 - Asia Pacific, 2006
In this paper seafloor classifications system based on artificial neural network (ANN) has been designed. The ANN architecture employed here is a combination of self organizing feature map (SOFM) and linear vector quantization (LVQ1). Currently acquired echo-waveform data acquired using single beam echo-sounder from twelve seafloor sediment locations ...
Bishwajit Chakraborty +3 more
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In this paper seafloor classifications system based on artificial neural network (ANN) has been designed. The ANN architecture employed here is a combination of self organizing feature map (SOFM) and linear vector quantization (LVQ1). Currently acquired echo-waveform data acquired using single beam echo-sounder from twelve seafloor sediment locations ...
Bishwajit Chakraborty +3 more
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Acoustic seafloor classification using machine learning and simulations of Helmholtz equations
The Journal of the Acoustical Society of America, 2018We discuss numerical methods for inverse problems in high frequency underwater acoustics, aiming to recover detailed characteristics of the seafloor from measured backscatter data generated from SONAR systems. The key to successful inversion is the use of accurate forward modeling capturing the dependence of backscatter on seafloor properties, such as ...
Christina Frederick +1 more
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Seafloor Sediment Classification using BP Neural Network
OCEANS 2021: San Diego – Porto, 2021Shu Gao, Xinmin Ren, Pan Qiao
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