Bayesian Seabed Classification Using Angle-Dependent Backscatter Data From Multibeam Echo Sounders

Landmark, Knut; Solberg, Anne; Austeng, Andreas; Hansen, Roy Edgar

doi:10.1109/joe.2013.2281133

Cited by 11 publications

(4 citation statements)

References 43 publications

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“…The echo sounder greatly improves the efficiency of seabed terrain detection. Additionally, it can be used to perform submarine substrate classification based on the echo intensities of sound waves reflected by different materials [ 23 , 24 ]. It has wide applications in marine resource exploration, gas hydrate detection, and other types of large-scale exploration.…”

Section: Acoustic Sonar Sensorsmentioning

confidence: 99%

Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning

Tsai

Lai

Sun

et al. 2021

Sensors

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Numerous sensors can obtain images or point cloud data on land, however, the rapid attenuation of electromagnetic signals and the lack of light in water have been observed to restrict sensing functions. This study expands the utilization of two- and three-dimensional detection technologies in underwater applications to detect abandoned tires. A three-dimensional acoustic sensor, the BV5000, is used in this study to collect underwater point cloud data. Some pre-processing steps are proposed to remove noise and the seabed from raw data. Point clouds are then processed to obtain two data types: a 2D image and a 3D point cloud. Deep learning methods with different dimensions are used to train the models. In the two-dimensional method, the point cloud is transferred into a bird’s eye view image. The Faster R-CNN and YOLOv3 network architectures are used to detect tires. Meanwhile, in the three-dimensional method, the point cloud associated with a tire is cut out from the raw data and is used as training data. The PointNet and PointConv network architectures are then used for tire classification. The results show that both approaches provide good accuracy.

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Section: Acoustic Sonar Sensorsmentioning

confidence: 99%

Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning

Tsai

Lai

Sun

et al. 2021

Sensors

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“…Dosso and co-workers successfully applied Bayesian techniques to estimate the number of seabed layers and the uncertainty of seabed parameters [24][25][26]. There have also been attempts to use Bayesian techniques to solve the problem of seabed sediment classification [27,28]. Bayesian inversion and model selection based on the geoacoustic models using sound speed and attenuation measurements were carried out by Bonomo and Isakson to compare the competing geoacoustic models [18].…”

Section: Of 30mentioning

confidence: 99%

A Comparison of Three Sediment Acoustic Models Using Bayesian Inversion and Model Selection Techniques

Zou

Zhai

et al. 2019

Remote Sensing

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Many geoacoustic models are used to establish the relationship between the physical and acoustic properties of sediments. In this work, Bayesian inversion and model selection techniques are applied to compare combinations of three geoacoustic models and corresponding scattering models—the fluid model with the effective density fluid model (EDFM), the grain-shearing elastic model with the viscosity grain-shearing (VGS(λ)) model, and the poroelastic model with the corrected and reparametrized extended Biot–Stoll (CREB) model. First, the resolution and correlation of parameters for the three models are compared based on estimates of the posterior probability distributions (PPDs), which are obtained by Bayesian inversion using the backscattering strength data. Then, model comparison and selection techniques are utilized to assess the matching degree of model predictions and measurements qualitatively and to ascertain the Bayes factors in favor of each quantitatively. These studies indicate that the fluid and poroelastic models outperform the grain-shearing elastic model, in terms of both parameter resolution and the ability to produce predictions in agreement with measurements for sandy sediments. The poroelastic model is considered to be the best, as the inversion based on it can provide more highly resolved information of sandy sediments. Finally, the attempt to implement geoacoustic inversion with different models provides a relatively feasible remote sensing scheme for various types of sediments under unknown conditions, which needs further validation.

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“…• including spatial information in the clustering algorithm, e.g. by adjusting the prior prob- The topmost curve corresponds to small incidence angles, the lowest curve to high angles (from [136]).…”

Section: Hybrid Acoustic Featuresmentioning

confidence: 99%