Evaluation of four supervised learning methods for benthic habitat mapping using backscatter from multi-beam sonar

Hasan, Rozaimi Che, Ierodiaconou, Daniel and Monk, Jacquomo 2012, Evaluation of four supervised learning methods for benthic habitat mapping using backscatter from multi-beam sonar, Remote sensing, vol. 4, no. 11, pp. 3427-3443, doi: 10.3390/rs4113427.

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Title Evaluation of four supervised learning methods for benthic habitat mapping using backscatter from multi-beam sonar
Author(s) Hasan, Rozaimi Che
Ierodiaconou, DanielORCID iD for Ierodiaconou, Daniel orcid.org/0000-0002-7832-4801
Monk, JacquomoORCID iD for Monk, Jacquomo orcid.org/0000-0002-1874-0619
Journal name Remote sensing
Volume number 4
Issue number 11
Start page 3427
End page 3443
Total pages 17
Publisher Molecular Diversity Preservation International
Place of publication Basel, Switzerland
Publication date 2012
ISSN 2072-4292
Keyword(s) biota
habitat map comparison
multibeam echo-sounder
quantitative backscatter classification
variable importance
Summary 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. © 2012 by the authors.
Language eng
DOI 10.3390/rs4113427
Field of Research 070499 Fisheries Sciences not elsewhere classified
060205 Marine and Estuarine Ecology (incl Marine Ichthyology)
090905 Photogrammetry and Remote Sensing
Socio Economic Objective 830299 Fisheries- Wild Caught not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30051599

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