Fuzzy clustering for seafloor classification

Marine Geology

Volumn 264, Issue 3-4, 2009, Pages 230-241

  Lucieer, V ^a   Lucieer, A ^a  

^a UNIVERSITY OF TASMANIA   (Australia)

Author keywords

cluster validity; fuzzy boundaries; fuzzy c means (FCM) clustering; marine mapping; uncertainty analysis

Indexed keywords

CLASSIFICATION MODELS; CLASSIFICATION RESULTS; CLUSTER VALIDITY; CLUSTERING TECHNIQUES; DATA SETS; FCM ALGORITHM; FUZZY BOUNDARIES; FUZZY C-MEANS; FUZZY C-MEANS (FCM) CLUSTERING; FUZZY C-MEANS ALGORITHMS; FUZZY CLUSTERING METHOD; GEOSCIENCE AUSTRALIA; GRID CELLS; ISODATA; MARINE BIODIVERSITY; MARINE MAPPING; MARINE SEDIMENTS; MEMBERSHIP VALUES; NEW RESULTS; OPTIMAL NUMBER; SEA FLOOR; SEABED MAPPING; SEAFLOOR SEDIMENTS; SHARP BOUNDARIES; SOFT SEDIMENTS; SPATIAL RELATIONSHIPS; SPATIAL REPRESENTATIONS; TRANSITION ZONES;

BIODIVERSITY; CELL MEMBRANES; CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; COPYING; FUZZY RULES; FUZZY SYSTEMS; MAPPING; PATTERN RECOGNITION; SEDIMENTOLOGY; SEDIMENTS; SUBMARINE GEOLOGY; UNCERTAINTY ANALYSIS;

FUZZY CLUSTERING;

ALGORITHM; CLASSIFICATION; FUZZY MATHEMATICS; MODEL VALIDATION; QUANTITATIVE ANALYSIS; SEAFLOOR; SEAFLOOR MAPPING; TRANSITION ZONE;

EID: 67650588771     PISSN: 00253227     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.margeo.2009.06.006     Document Type: Article

References (53)

1. Baraldi A., and Blonda P. A survey of fuzzy clustering algorithms for pattern recognition - part 1. IEEE Trans. 29 (1999) 778-785
2. Bezdek J.C. Cluster validity with fuzzy sets. J. Cybern. 3 (1974) 58-73
3. Bezdek J.C. Computing with uncertainty. IEEE Commun. Mag. (1992) 24-36
4. Bezdek J.C., Erhlich R., and Full W. FCM: The fuzzy c-means clustering algorithm. Comput. Geosci. 10 (1984) 191-203
5. Bie S.W., and Beckett P.H.T. Comparison of four independent soil surveys by air photo interpretation, Paphos area (Cyprus). Photogrammetria 29 (1973) 189-202
6. Burrough P.A., and Frank A.U. Geographic Objects with Indeterminate Boundaries, Number 2 in GISDATA (1996), Taylor and Francis
7. Burrough P.A., and McDonnell R.A. Principles of geographical information systems. Principles of Geographical Information Systems (1998), Oxford University Press, USA
8. Burrough P.A., and Webster R. Improving a reconnaissance soil classification by multivariate methods. J. Soil Sci. 27 (1976) 554-571
9. Burrough P.A., van Gaans P.F.M., and Hootsmans R.J. Continuous classification in soil survey: spatial correlation, confusion and boundaries. Geoderma (1997) 115-135
10. Burrough P.A., van Gaans P., and MacMillan R. High-resolution landform classification using fuzzy k-means. Fuzzy Sets Syst. 113 (2000) 37-52
11. Canty, M.J., 2007. Image Analysis, Classification and Change Detection in Remote Sensing with Algorithms for ENVI/IDL. CRC Press, Taylor & Francis Group, Boca Raton.
12. Calinski T., and Harabasz J. A dendrite method for cluster analysis. Commun. Stat. 3 (1974) 1-27
13. Dave R.N. Validating fuzzy partition obtained through c-shells clustering. Pattern Recogn. 17 (1996) 613-623
14. Davies D.L., and Bouldin D.W. A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 1 (1979) 224-227
15. Duda T., and Canty M.J. Unsupervised classification of satellite imagery: choosing a good algorithm. Int. J. Remote Sens. 23 (2002) 2193-2212
16. Duda, R.O., Hart, P.E., Stork, D.G., 2001. Pattern Classification. 2nd Edition. p. 680. Wiley.
17. Dunn J.C. A fuzzy relative of the isodata process and its use in detecting compact well separated clusters. J. Cybern. 28 (1973) 32-57
18. Fisher P., and Wood J. What is a Mountain ? Or the Englishman who went up a Boolean geographical concept but realised it was fuzzy. Geography 83 (1998) 247-256
19. Gath I., and Geva A.B. Unsupervised optimal fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 11 (1989) 773-781
20. Goff J.A., Kraft B.J., Mayer L.A., Schock S.G., Sommerfield C.K., Olson H.C., Gulick S.P., and Nordfjord S. Seabed characterisation on the New Jersey middle and outer shelf: correlatability and spatial variability of seafloor sediment properties. Mar. Geol. 209 (2004) 147-172
21. Greene H.G., Yoklavich M.M., Sullivan D.E., and Cailliet G.M. A geophysical approach to classifying marine benthic habitats: Monterey Bay as a model. Applications of sidescan sonar and laser line systems in fisheries research. Alask. Fish Game Spec. Publ. 9 (1995) 15-30
22. Hall S.J. Physical disturbance and marine benthic communities; life in unconsolidated sediments. Oceanogr. Mar. Biol. Ann. Rev. 32 (1994) 179-239
23. Heap A.D., Harris P.T., Hinde A., and Woods M. Benthic Marine Bioregionalisation of Australia's Exclusive Economic Zone (2005) ISBN 1-977043-60-5
24. Höppner F., Klawonn F., Kruse F., and Runkler T. Fuzzy Cluster Analysis (1999), Wiley, Chichester
25. ITTVIS, 2008. ITT Visual Information Solutions - IDL-ENVI. http://www.ittvis.com, Last accessed: May 2008.
26. ITTVIS, 2009. ITT Visual Information Solutions, Image Processing & Data Analysis - IDL/ENVI. http://www.ittvis.com, last accessed: 25 June 2009.
27. Kostylev V.E., Todd B.J., Fader G.B.J., Courtney R.C., Cameron G.D.M., and Pickrill R.A. Benthic habitat mapping on the Scotian Shelf based on multibeam bathymetry, surficial geology and sea floor photographs. Mar. Ecol. Prog. Ser. 219 (2001) 121-137
28. Kostylev V.E., Courtney R.C., Robert G., and Todd B.J. Stock evaluation of giant scallop (Placopecten magellanicus) using high-resolution acoustics for seabed mapping. Fish. Res. 60 (2003) 479-492
29. McBratney A.B., and Odeh I.O.A. Application of fuzzy sets in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions. Geoderma 77 (1997) 85-114
30. McBratney A.B., de Gruijter J.J., and Brus D.J. Spatial prediction and mapping of continuous soil classes. Geoderma 54 (1992) 39-64
31. Menge B.A. Predation intensity in a rocky intertidal community; relation between predator foraging activity and environmental harshness. Oecologia (Berl.) 34 (1978) 1-16
32. Odeh, I.O.A., McBratney, A.B., Chittleborough, D.J., 1990. Design of optimal sample spacings for mapping soil using fuzzy-k-means and regionalized variable theory. Geoderma 47(1-2), 93-122.
33. Orpin A.R., and Kostylev V.E. Towards a statistically valid method of textural sea floor characterisation of benthic habitat. Mar. Geol. 225 (2006) 209-222
34. Pakhira M.K., Bandyopadhyay S., and Maulik U. A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification. Fuzzy Sets and Syst. 155 (2005) 191-214
35. Pinn E.H., Robertson M.R., Shand C.W., and Armstrong F. Broad-scale benthic community analysis in the Greater Minch Area (Scottish West Coast) using remote and non destructive techniques. Int. J. Remote Sens. 19 (1998) 3039-3054
36. Post A., Wassenberg T., and Passlow V. Physical surrogates for macrofaunal distributions and abundance in a tropical gulf. Mar. Freshw. Res. 57 (2006) 469-483
37. Richards J.A., and Jia X. Remote Sensing Digital Image Analysis - An Introduction (2005), Springer, Berlin
38. Snelgrove P.V.R. The biodiversity of macrofaunal organisms in marine sediments. Biodivers. Conserv. 7 (1998) 1123-1132
39. Snelgrove P.V.R. Getting to the bottom of marine biodiversity: sedimentary habitats. BioScience 49 (1999) 2129-2138
40. Snelgrove P.V.R., and Butman C.A. Animal sediment relationships revisited: cause versus effect. Oceanogr. Mar. Biol. Ann. Rev. 32 (1994) 111-127
41. Sotheran I.S., Foster-Smith R.L., and Davies J. Mapping of marine benthic habitats using image processing techniques within a raster-based geographic information system. Estuar. Coast. Shelf Sci. 44 (1997) 25-31
42. Todd B.J., Fader G.B.J., Courtney R.C., and Pickrill R.A. Quaternary geology and surficial sediment processes; Browns Bank, Scotian Shelf; based on multibeam bathymetry. Mar. Geol. 162 (1999) 165-214
43. Tsekouras G., Sarimveis H., Kavakli E., and Bafas G. A hierarchical fuzzy-clustering approach to fuzzy modeling. Fuzzy Sets and Syst. 150 (2005) 245-266
44. Underwood A.J., Chapman M.G., and Connell S.D. Observations in ecology; you can't make progress on the processes without understanding the patterns. J. Exp. Mar. Biol. Ecol. (1996) 97-115
45. Vriend S.P., van Gaans P.F.M., Middelburg J., and de Nijs A. The application of fuzzy c-means cluster analysis and non-linear mapping to geochemical datasets: examples from Portugal. Appl. Geochem. 3 (1988) 213-224
46. Wang W., and Zang Y. On fuzzy cluster validity indices. Fuzzy Sets and Syst. 158 (2007) 2095-2117
47. Whiteway, T., Heap, A.D., Lucieer, V., Hinde, A., Ruddick, R., and Harris, P.T., 2007. Seascapes of the Australia margin and adjacent sea floor: methodology and results. Geoscience Australia, Record 2007/11, 133 pp.
48. Wu K.L., and Yang M. Alternative c-means clustering algorithms. Pattern Recogn. 35 (2002) 2267-2278
49. Wu K.L., and Yang M. A cluster validity index for fuzzy clustering. Pattern Recogn. Lett. 26 (2005) 1275-1291
50. Xie X.L., and Beni G. A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. 13 (1991) 841-847
51. Yang M.S. A survey of fuzzy clustering. Math. Comput. Model. 18 (1993) 1-6
52. Yang M.S., and Ko C.H. On a class of fuzzy c-numbers clustering procedures for fuzzy data. Fuzzy Sets Syst. 84 (1996) 49-60
53. Zadeh L.A. Fuzzy sets. Inf. Control 8 (1965) 338-353