Combining angular response classification and backscatter imagery segmentation for benthic biological habitat mapping

Backscatter information from multibeam echosounders (MBES) have been shown to contain useful information for the characterisation of benthic habitats. Compared to backscatter imagery, angular response of backscatter has shown advantages for feature discrimination. However its low spatial resolution...

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Main Authors: Che Hasan, Rozaimi, Ierodiaconou, Daniel, Laurenson, Laurie
Format: Article
Published: Academic Press 2011
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Online Access:http://eprints.utm.my/id/eprint/44790/
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spelling my.utm.447902017-09-13T04:17:53Z http://eprints.utm.my/id/eprint/44790/ Combining angular response classification and backscatter imagery segmentation for benthic biological habitat mapping Che Hasan, Rozaimi Ierodiaconou, Daniel Laurenson, Laurie TR Photography Backscatter information from multibeam echosounders (MBES) have been shown to contain useful information for the characterisation of benthic habitats. Compared to backscatter imagery, angular response of backscatter has shown advantages for feature discrimination. However its low spatial resolution inhibits the generation of fine scale habitat maps. In this study, angular backscatter response was combined with image segmentation of backscatter imagery to characterise benthic biological habitats in Discovery Bay Marine National Park, Victoria, Australia. Angular response of backscatter data from a Reson Seabat 8101 MBES (240 kHz) was integrated with georeferenced underwater video observations for constructing training data. To produce benthic habitat maps, decision tree supervised classification results were combined with mean shift image segmentation for class assignment. The results from mean angular response characteristics show effects of incidence angle at the outer angle for invertebrates (INV) and mixed red and invertebrates (MRI) classes, whilst mixed brown algae (MB) and mixed brown algae and invertebrates (MBI) showed similar responses independent from incidence angle. Automatic segmentation processing produce over segmented results but showed good discrimination between heterogeneous regions. Accuracy assessment from habitat maps produced overall accuracies of 79.6% (Kappa coefficient = 0.66) and 80.2% (Kappa coefficient = 0.67) for biota and substratum classifications respectively. MRI and MBI produced the lowest average accuracy while INV the highest. The ability to combine angular response and backscatter imagery provides an alternative approach for investigating biological information from acoustic backscatter data. Academic Press 2011 Article PeerReviewed Che Hasan, Rozaimi and Ierodiaconou, Daniel and Laurenson, Laurie (2011) Combining angular response classification and backscatter imagery segmentation for benthic biological habitat mapping. Estuarine, Coastal and Shelf Science, 97 . pp. 1-9. ISSN 0272-7714
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TR Photography
spellingShingle TR Photography
Che Hasan, Rozaimi
Ierodiaconou, Daniel
Laurenson, Laurie
Combining angular response classification and backscatter imagery segmentation for benthic biological habitat mapping
description Backscatter information from multibeam echosounders (MBES) have been shown to contain useful information for the characterisation of benthic habitats. Compared to backscatter imagery, angular response of backscatter has shown advantages for feature discrimination. However its low spatial resolution inhibits the generation of fine scale habitat maps. In this study, angular backscatter response was combined with image segmentation of backscatter imagery to characterise benthic biological habitats in Discovery Bay Marine National Park, Victoria, Australia. Angular response of backscatter data from a Reson Seabat 8101 MBES (240 kHz) was integrated with georeferenced underwater video observations for constructing training data. To produce benthic habitat maps, decision tree supervised classification results were combined with mean shift image segmentation for class assignment. The results from mean angular response characteristics show effects of incidence angle at the outer angle for invertebrates (INV) and mixed red and invertebrates (MRI) classes, whilst mixed brown algae (MB) and mixed brown algae and invertebrates (MBI) showed similar responses independent from incidence angle. Automatic segmentation processing produce over segmented results but showed good discrimination between heterogeneous regions. Accuracy assessment from habitat maps produced overall accuracies of 79.6% (Kappa coefficient = 0.66) and 80.2% (Kappa coefficient = 0.67) for biota and substratum classifications respectively. MRI and MBI produced the lowest average accuracy while INV the highest. The ability to combine angular response and backscatter imagery provides an alternative approach for investigating biological information from acoustic backscatter data.
format Article
author Che Hasan, Rozaimi
Ierodiaconou, Daniel
Laurenson, Laurie
author_facet Che Hasan, Rozaimi
Ierodiaconou, Daniel
Laurenson, Laurie
author_sort Che Hasan, Rozaimi
title Combining angular response classification and backscatter imagery segmentation for benthic biological habitat mapping
title_short Combining angular response classification and backscatter imagery segmentation for benthic biological habitat mapping
title_full Combining angular response classification and backscatter imagery segmentation for benthic biological habitat mapping
title_fullStr Combining angular response classification and backscatter imagery segmentation for benthic biological habitat mapping
title_full_unstemmed Combining angular response classification and backscatter imagery segmentation for benthic biological habitat mapping
title_sort combining angular response classification and backscatter imagery segmentation for benthic biological habitat mapping
publisher Academic Press
publishDate 2011
url http://eprints.utm.my/id/eprint/44790/
_version_ 1643651545823182848
score 13.18916