Construction of seafloor thematic maps from multibeam acoustic backscatter angular response data

Rzhanov, Yuri; Fonseca, Luciano E.; Mayer, Larry A.

doi:10.1016/j.cageo.2011.09.001

Cited by 45 publications

(35 citation statements)

References 14 publications

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“…This study extends the approach presented by Rzhanov et al [14] combining the angular response analysis with information from the segmented backscatter image for characterizing biota habitats. An angular based approach takes advantage of the variation in acoustic response between habitat variables defined across the entire angular range.…”

Section: Discussionmentioning

confidence: 68%

“…The homogenous region is constructed using human visual interpretation by manually grouping areas assumed to have similar backscatter texture. In a recent study, automated delineation technique has been proposed because the manual approach may produce inconsistent results due to the human errors [14]. They used an automated spatial image segmentation process for the backscatter imagery to improve the resolution of the angular response classification and prediction, based on comparing backscatter values in pixels to template backscatter curves, which are those occurring most often in the data set.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Four Supervised Learning Methods for Benthic Habitat Mapping Using Backscatter from Multi-Beam Sonar

2012

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An understanding of the distribution and extent of marine habitats is essential for the implementation of ecosystem-based management strategies. Historically this had been difficult in marine environments until the advancement of acoustic sensors. This study demonstrates the applicability of supervised learning techniques for benthic habitat characterization using angular backscatter response data. With the advancement of multibeam echo-sounder (MBES) technology, full coverage datasets of physical structure over vast regions of the seafloor are now achievable. Supervised learning methods typically applied to terrestrial remote sensing provide a cost-effective approach for habitat characterization in marine systems. However the comparison of the relative performance of different classifiers using acoustic data is limited. Characterization of acoustic backscatter data from MBES using four different supervised learning methods to generate benthic habitat maps is presented. Maximum Likelihood Classifier (MLC), Quick, Unbiased, Efficient Statistical Tree (QUEST), Random Forest (RF) and Support Vector Machine (SVM) were evaluated to classify angular backscatter response into habitat classes using training data acquired from underwater video observations. Results for biota classifications indicated that SVM and RF produced the highest accuracies, followed by QUEST and MLC, respectively. The most important backscatter data were from the moderate incidence angles between 30 and 50. This study presents initial results for understanding how acoustic backscatter from MBES can be optimized for the characterization of marine benthic biological habitats.

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Section: Discussionmentioning

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Evaluation of Four Supervised Learning Methods for Benthic Habitat Mapping Using Backscatter from Multi-Beam Sonar

2012

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“…The angular dependence is a fundamental characteristic of the backscatter response (Le Chenadec et al 2007;Clarke et al 1997), which imply specific operations in the data processing (Augustin and Lurton 2005;de Moustier 1986;Hellequin et al 2003;Gravilov 2011;Schimel et al 2015) and complexity in their interpretation (Hasan et al 2012;Dugelay et al 1996;Fonseca and Mayer 2007;Huang et al 2013;Lamarche et al 2011;Rzhanov et al 2012). A rough and hard seafloor interface, usually from coarse material or rocks results in isotropic scattering of the sound waves so that the average Fig.…”

Section: Backscatter Data Processingmentioning

confidence: 99%

Recommendations for improved and coherent acquisition and processing of backscatter data from seafloor-mapping sonars

Lamarche

Lurton

2017

Mar Geophys Res

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“…New image segmentation methods have also provided a tool to reduce this constraint with AR analysis (Rzhanov et al 2012;Che Hasan et al 2014. Our Point Cloates study area contains seabed substrate that changes abruptly from reef to unconsolidated sediment .…”

Section: Introductionmentioning

confidence: 99%

Insights into environmental drivers of acoustic angular response using a self-organising map and hierarchical clustering

et al. 2015

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Acoustic backscatter from the seafloor is a complex function of signal frequency, seabed roughness, grain size distribution, benthos, bioturbation, volume reverberation, and other factors. Angular response is the variation in acoustic backscatter with incident angle and is considered be an intrinsic property of the seabed. An unsupervised classification technique combining a self-organising map (SOM) and hierarchical clustering was used to create an angular response facies map and explore the relationships between acoustic facies and ground truth data. Cluster validation routines indicated that a two cluster solution was optimal and separated sediment dominated environments from mixtures of sediment and hard ground. Low cluster separation limited cluster validation routines from identifying fine cluster structure visible with an AR density plot. Cluster validation, aided by a visual comparison with an AR density plot, indicated that a 14 cluster solution was also a suitable representation of the input dataset. Clusters that were a mixture of hard and unconsolidated substrates displayed an increase in backscatter with an increase in the occurrence of hard ground and highlighted the sensitivity of AR curves to the presence of even modest amounts of hard ground. Remapping video observations and sediment data onto the SOM matrix is innovative and depicts the relationship between ground truth data and cluster structure. Mapping environmental variables onto the SOM matrix can show broad trends and localised peaks and troughs and display the variability of ground truth data within designated clusters. These variables, when linked to AR curves via clusters, can indicate how environmental factors influence the shape of the curves. Once these links are established they can be incorporated into improved geoacoustic models that replicate field observations.

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Construction of seafloor thematic maps from multibeam acoustic backscatter angular response data

Cited by 45 publications

References 14 publications

Evaluation of Four Supervised Learning Methods for Benthic Habitat Mapping Using Backscatter from Multi-Beam Sonar

Evaluation of Four Supervised Learning Methods for Benthic Habitat Mapping Using Backscatter from Multi-Beam Sonar

Recommendations for improved and coherent acquisition and processing of backscatter data from seafloor-mapping sonars

Insights into environmental drivers of acoustic angular response using a self-organising map and hierarchical clustering

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