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Fuzzy clustering for seafloor classification.

Lucieer, A and Lucieer, VL 2009 , 'Fuzzy clustering for seafloor classification.' , Marine Geology, vol. 264, no. 3-4 , pp. 230-240 , doi:

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In order to develop quantitative seafloor sediment classification techniques it is important to acknowledge
that by nature the boundaries between soft sediments are characterized by transition zones and therefore are
indeterminate and gradual. A fuzzy clustering method, fuzzy c-means (FCM), was used to identify these
transition zones within a subset of the data used to generate the Australian Seascapes classification model.
The overlapping classes and gradual boundaries resulting from the fuzzy c-means algorithm provided
estimates of sediment boundaries that are a closer model of reality than sharp boundaries. FCM output is
given in the form of membership layers for each class, hard classes for each grid cell based on the maximum
membership value, and a confusion index layer quantifying uncertainty in class attribution. The confusion
index layer provided a spatial representation of transition zones and overlap between seafloor classes and
highlighted areas of greatest uncertainty. We extended the standard FCM algorithm by applying the new
FMLE fuzzy clustering algorithm that takes into account spatial relationships in the data. In addition, we
implemented and applied new cluster validity techniques, PCAES, PBMF, and XB to determine the optimal
number of clusters in the data, which is a novel pattern recognition application for seabed mapping. The 5-
class FCM classification provided the most reliable result. The results of this research were tested and
validated on a simulated dataset and then the clustering and validation algorithms were applied to marine
sediment data to identify Seascapes. The new results were compared with previously published Seascapes
classes identified with hard ISODATA clustering techniques from GeoScience Australia's Seascapes
classification result. With the increasing use of physical surrogates to explain marine biodiversity, this
research plays a crucial role in the development of techniques to identify habitat zones on the seabed.

Item Type: Article
Authors/Creators:Lucieer, A and Lucieer, VL
Keywords: marine mapping fuzzy boundaries fuzzy c-means (FCM) clustering cluster validity uncertainty analysis
Journal or Publication Title: Marine Geology
ISSN: 0025-3227
DOI / ID Number:
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