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Benthic habitat mapping by autonomous underwater vehicles

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Davie, A (2013) Benthic habitat mapping by autonomous underwater vehicles. PhD thesis, University of Tasmania.

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Abstract

This work presents a functional system for performing unsupervised classification
and mapping of benthic habitats using an autonomous underwater vehicle.
Traditionally, tne-scale underwater mapping has been an expensive process,
inaccurate, and is performed infrequently. This work provides contributions
in three main areas: the control systems allowing the underwater vehicle to
perform autonomous measurements using inexpensive sensors; implementation of
a memetic algorithm for unmixing hyper-spectral signals to identify and classify
habitat types; detection of new end-member types, and the production of accurate
maps, while mitigating for constraints in vehicle capabilities. Meta-data is recorded
and associated with detected end-member types to act as an aid in expert
classification.
The production of multi-layer maps is demonstrated. These maps account for
uncertainty in the vehicle's position, and variability in unmixing accuracy and
confidence in data quality. This system produces end-member library spectra,
maps showing end-member location and abundance, bathymetry, coverage maps,
and a confidence map. An analysis of the accuracy of the algorithms using image-
processing techniques showing the strength of the mapping algorithms in the
presence of noise is presented.
This work has developed techniques to support fine-scale benthic classification
using underwater vehicles. This capability can complement existing remote sensing
techniques, allowing mapping where benthic habitat is obscured from airborne and
satellite sensors because of water clarity and depth. The techniques for accurate
and fine-scale measurement of benthic mixtures proposed here offer alternative
tools for the monitoring and management of estuarine environments.

Item Type: Thesis (PhD)
Keywords: hyperspectral, AUV, unmix, mapping, robotic
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Date Deposited: 05 Feb 2014 03:35
Last Modified: 15 Sep 2017 01:06
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