Polymetallic nodules abundance estimation using sidescan sonar: A quantitative approach using artificial neural network

Wong, Liang Jie; Kalyan, Bharath; Vishnu, Hari

doi:10.1109/oceanse.2017.8084857

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…Jie et al [99] presented an Artificial Neural Network (ANN) with a two-hidden-layer feed-forward topology network to estimate polymetallic nodule abundance using side-scan backscatter from the Clarion and Clipperton Fracture Zone (CCFZ) in the Pacific Ocean, obtaining average accuracy of 84.20%. Later, the method was improved by Wong et al [100] by adding bathymetry data.…”

Section: Deep Learningmentioning

confidence: 99%

A Survey of Seafloor Characterization and Mapping Techniques

Loureiro,

Dias,

Almeida

et al. 2024

Remote Sensing

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The deep seabed is composed of heterogeneous ecosystems, containing diverse habitats for marine life. Consequently, understanding the geological and ecological characteristics of the seabed’s features is a key step for many applications. The majority of approaches commonly use optical and acoustic sensors to address these tasks; however, each sensor has limitations associated with the underwater environment. This paper presents a survey of the main techniques and trends related to seabed characterization, highlighting approaches in three tasks: classification, detection, and segmentation. The bibliography is categorized into four approaches: statistics-based, classical machine learning, deep learning, and object-based image analysis. The differences between the techniques are presented, and the main challenges for deep sea research and potential directions of study are outlined.

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Section: Deep Learningmentioning

confidence: 99%

A Survey of Seafloor Characterization and Mapping Techniques

Loureiro,

Dias,

Almeida

et al. 2024

Remote Sensing

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“…They result mainly from the large distances between sampling stations (which vary depending on the stage of recognition of the nodule-bearing areas), high variability of nodule abundance, and to a lesser extent, from the inevitable errors during the sampling process [20]. Therefore, the attempts to combine the estimation of nodule abundance based on the classical direct sampling (e.g., using box corers) [15,21] with indirect methods, such as photographic surveys [22][23][24][25] or widely understood hydro-acoustic methods [26,27], seem natural and rational.…”

Section: Introductionmentioning

confidence: 99%

Application of General Linear Models (GLM) to Assess Nodule Abundance Based on a Photographic Survey (Case Study from IOM Area, Pacific Ocean)

Wasilewska-Błaszczyk

Mucha

2021

Minerals

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The success of the future exploitation of the Pacific polymetallic nodule deposits depends on an accurate estimation of their resources, especially in small batches, scheduled for extraction in the short term. The estimation based only on the results of direct seafloor sampling using box corers is burdened with a large error due to the long sampling interval and high variability of the nodule abundance. Therefore, estimations should take into account the results of bottom photograph analyses performed systematically and in large numbers along the course of a research vessel. For photographs taken at the direct sampling sites, the relationship linking the nodule abundance with the independent variables (the percentage of seafloor nodule coverage, the genetic types of nodules in the context of their fraction distribution, and the degree of sediment coverage of nodules) was determined using the general linear model (GLM). Compared to the estimates obtained with a simple linear model linking this parameter only with the seafloor nodule coverage, a significant decrease in the standard prediction error, from 4.2 to 2.5 kg/m2, was found. The use of the GLM for the assessment of nodule abundance in individual sites covered by bottom photographs, outside of direct sampling sites, should contribute to a significant increase in the accuracy of the estimation of nodule resources.

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Application of machine learning in ocean data

Lou

Dang

et al. 2021

Multimedia Systems

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Polymetallic nodules abundance estimation using sidescan sonar: A quantitative approach using artificial neural network

Cited by 6 publications

References 11 publications

A Survey of Seafloor Characterization and Mapping Techniques

A Survey of Seafloor Characterization and Mapping Techniques

Application of General Linear Models (GLM) to Assess Nodule Abundance Based on a Photographic Survey (Case Study from IOM Area, Pacific Ocean)

Application of machine learning in ocean data

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