Predictive Mapping of Seabed Cover Types Using Angular Response Curves of Multibeam Backscatter Data: Testing Different Feature Analysis Approaches  

Authors / CoAuthors

Huang, Z. | Siwabessy, J. | Nichol, S. | Anderson, T. | Brooke, B.

Abstract

This study used angular response curves of multibeam backscatter data to predict the distributions of seven seabed cover types in an acoustically-complex area. Several feature analysis approaches on the angular response curves were examined. A Probability Neural Network model was chosen for the predictive mapping. The prediction results have demonstrated the value of angular response curves for seabed mapping with a Kappa coefficient of 0.59. Importantly, this study demonstrated the potential of various feature analysis approaches to improve the seabed mapping. For example, the approach to derive meaningful statistical parameters from the curves achieved significant feature reduction and some performance gain (e.g., Kappa = 0.62). The first derivative analysis approach achieved the best overall statistical performance (e.g., Kappa = 0.84); while the approach to remove the global slope produced the best overall prediction map (Kappa = 0.74). We thus recommend these three feature analysis approaches, along with the original angular response curves, for future similar studies.

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nonGeographicDataset

eCat Id

73016

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Keywords

• External PublicationArticle

( Theme )

• backscatter

( Theme )

• mapping

( Theme )

• seabed

( Theme )

• sedimentology

( Theme )

• marine

Australian and New Zealand Standard Research Classification (ANZSRC)

• Earth Sciences

• Published_Internal

Publication Date

2011-10-27T00:00:00

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Australian Government Security ClassificationSystem    

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Creative Commons Attribution 4.0 International Licence    

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geoscientificInformation

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This paper was written based on the research results of CERF Marine Biodiversity Hub.

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CERF Marine Biodiversity Hub