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PLOS ONE  2014 

A Comparison of Supervised Classification Methods for the Prediction of Substrate Type Using Multibeam Acoustic and Legacy Grain-Size Data

DOI: 10.1371/journal.pone.0093950

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Abstract:

Detailed seabed substrate maps are increasingly in demand for effective planning and management of marine ecosystems and resources. It has become common to use remotely sensed multibeam echosounder data in the form of bathymetry and acoustic backscatter in conjunction with ground-truth sampling data to inform the mapping of seabed substrates. Whilst, until recently, such data sets have typically been classified by expert interpretation, it is now obvious that more objective, faster and repeatable methods of seabed classification are required. This study compares the performances of a range of supervised classification techniques for predicting substrate type from multibeam echosounder data. The study area is located in the North Sea, off the north-east coast of England. A total of 258 ground-truth samples were classified into four substrate classes. Multibeam bathymetry and backscatter data, and a range of secondary features derived from these datasets were used in this study. Six supervised classification techniques were tested: Classification Trees, Support Vector Machines, k-Nearest Neighbour, Neural Networks, Random Forest and Naive Bayes. Each classifier was trained multiple times using different input features, including i) the two primary features of bathymetry and backscatter, ii) a subset of the features chosen by a feature selection process and iii) all of the input features. The predictive performances of the models were validated using a separate test set of ground-truth samples. The statistical significance of model performances relative to a simple baseline model (Nearest Neighbour predictions on bathymetry and backscatter) were tested to assess the benefits of using more sophisticated approaches. The best performing models were tree based methods and Naive Bayes which achieved accuracies of around 0.8 and kappa coefficients of up to 0.5 on the test set. The models that used all input features didn't generally perform well, highlighting the need for some means of feature selection.

References

[1]  Wynn RB, Bett BJ, Evans AJ, Griffiths G, Huvenne VAI, et al. (2012) Investigating the feasibility of utilizing AUV and Glider technology for mapping and monitoring of the UK MPA network. Southampton: National Oceanography Centre. 244 p.
[2]  Anderson JT, Van Holliday D, Kloser R, Reid DG, Simard Y (2008) Acoustic seabed classification: current practice and future directions. ICES Journal of Marine Science 65: 1004–1011. doi: 10.1093/icesjms/fsn061
[3]  Brown CJ, Smith SJ, Lawton P, Anderson JT (2011) Benthic habitat mapping: A review of progress towards improved understanding of the spatial ecology of the seafloor using acoustic techniques. Estuarine, Coastal and Shelf Science 92: 502–520. doi: 10.1016/j.ecss.2011.02.007
[4]  Blondel P, Gomez Sichi O (2009) Textural analyses of multibeam sonar imagery from Stanton Banks, Northern Ireland continental shelf. Applied Acoustics 70: 1288–1297. doi: 10.1016/j.apacoust.2008.07.015
[5]  Brown CJ, Collier JS (2008) Mapping benthic habitat in regions of gradational substrata: An automated approach utilising geophysical, geological, and biological relationships. Estuarine Coastal and Shelf Science 78: 203–214. doi: 10.1016/j.ecss.2007.11.026
[6]  Brown CJ, Sameoto JA, Smith SJ (2012) Multiple methods, maps, and management applications: Purpose made seafloor maps in support of ocean management. Journal of Sea Research 72: 1–13. doi: 10.1016/j.seares.2012.04.009
[7]  Lathrop RG, Cole M, Senyk N, Butman B (2006) Seafloor habitat mapping of the New York Bight incorporating sidescan sonar data. Estuarine, Coastal and Shelf Science 68: 221–230. doi: 10.1016/j.ecss.2006.01.019
[8]  McGonigle C, Brown C, Quinn R, Grabowski J (2009) Evaluation of image-based multibeam sonar backscatter classification for benthic habitat discrimination and mapping at Stanton Banks, UK. Estuarine Coastal and Shelf Science 81: 423–437. doi: 10.1016/j.ecss.2008.11.017
[9]  Milligan GW, Cooper MC (1985) An examination of procedures for determining the number of clusters in a data set. Psychometrika 50: 159–179. doi: 10.1007/bf02294245
[10]  Lathrop RG, Cole M, Senyk N, Butman B (2006) Seafloor habitat mapping of the New York Bight incorporating sidescan sonar data. Estuarine Coastal and Shelf Science 68: 221–230. doi: 10.1016/j.ecss.2006.01.019
[11]  Alpaydin E (2010) Introduction to Machine Learning. Cambridge, MA: MIT Press. 537 p.
[12]  Buhl-Mortensen P, Dolan M, Buhl-Mortensen L (2009) Prediction of benthic biotopes on a Norwegian offshore bank using a combination of multivariate analysis and GIS classification. Ices Journal of Marine Science 66: 2026–2032. doi: 10.1093/icesjms/fsp200
[13]  Ierodiaconou D, Monk J, Rattray A, Laurenson L, Versace VL (2011) Comparison of automated classification techniques for predicting benthic biological communities using hydroacoustics and video observations. Continental Shelf Research 31: S28–S38. doi: 10.1016/j.csr.2010.01.012
[14]  Che Hasan R, Ierodiaconou D, Monk J (2012) Evaluation of Four Supervised Learning Methods for Benthic Habitat Mapping Using Backscatter from Multi-Beam Sonar. Remote Sensing 4: 3427–3443. doi: 10.3390/rs4113427
[15]  Lucieer V, Hill NA, Barrett NS, Nichol S (2013) Do marine substrates ‘look’ and ‘sound’ the same? Supervised classification of multibeam acoustic data using autonomous underwater vehicle images. Estuarine, Coastal and Shelf Science 117: 94–106. doi: 10.1016/j.ecss.2012.11.001
[16]  Dartnell P, Gardner JV (2004) Predicting seafloor facies from multibeam bathymetry and backscatter data. Photogrammetric Engineering and Remote Sensing 70: 1081–1091. doi: 10.14358/pers.70.9.1081
[17]  Rattray A, Ierodiaconou D, Laurenson L, Burq S, Reston M (2009) Hydro-acoustic remote sensing of benthic biological communities on the shallow South East Australian continental shelf. Estuarine Coastal and Shelf Science 84: 237–245. doi: 10.1016/j.ecss.2009.06.023
[18]  Rooper CN, Zimmermann M (2007) A bottom-up methodology for integrating underwater video and acoustic mapping for seafloor substrate classification. Continental Shelf Research 27: 947–957. doi: 10.1016/j.csr.2006.12.006
[19]  Che Hasan R, Ierodiaconou D, Laurenson L (2012) Combining angular response classification and backscatter imagery segmentation for benthic biological habitat mapping. Estuarine Coastal and Shelf Science 97: 1–9. doi: 10.1016/j.ecss.2011.10.004
[20]  Ojeda GY, Gayes PT, Van Dolah RF, Schwab WC (2004) Spatially quantitative seafloor habitat mapping: example from the northern South Carolina inner continental shelf. Estuarine Coastal and Shelf Science 59: 399–416. doi: 10.1016/j.ecss.2003.09.012
[21]  Marsh I, Brown C (2009) Neural network classification of multibeam backscatter and bathymetry data from Stanton Bank (Area IV). Applied Acoustics 70: 1269–1276. doi: 10.1016/j.apacoust.2008.07.012
[22]  Simons DG, Snellen M (2009) A Bayesian approach to seafloor classification using multi-beam echo-sounder backscatter data. Applied Acoustics 70: 1258–1268. doi: 10.1016/j.apacoust.2008.07.013
[23]  Folk RL (1954) The distinction between grain size and mineral composition in sedimentary-rock nomenclature. Journal of Geology 62: 344–359. doi: 10.1086/626171
[24]  Wilson MFJ, O'Connell B, Brown C, Guinan JC, Grehan AJ (2007) Multiscale Terrain Analysis of Multibeam Bathymetry Data for Habitat Mapping on the Continental Slope. Marine Geodesy 30: 3–35. doi: 10.1080/01490410701295962
[25]  Holmes KW, Van Niel KP, Radford B, Kendrick GA, Grove SL (2008) Modelling distribution of marine benthos from hydroacoustics and underwater video. Continental Shelf Research 28: 1800–1810. doi: 10.1016/j.csr.2008.04.016
[26]  Venables W, Ripley B (2002) Modern Applied Statisitcs with S-PLUS. New York: Springer. 495 p.
[27]  R Development Core Team (2011) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. 1706 p.
[28]  Luts J, Ojeda F, Plas R, Van De Moor B, De Huffel S, et al. (2010) A tutorial on support vector machine-based methods for classification problems in chemometrics. Analytica Chimica Acta 665: 129–145. doi: 10.1016/j.aca.2010.03.030
[29]  Meyer D (2012) Support Vector Machines: The Interface to libsvm pacakge e1071. Technische Universit?t Wien, Austria. 8 p.
[30]  Therneau T, Atkinson E (1997) An Introduction to Recursive Partitioning Using the rpart Routine. Rochester: Section of Biostatistics, Mayo Clinic. 52 p.
[31]  Breiman L (2001) Random Forests. Machine Learning 45: 5–32. doi: 10.1023/a:1010933404324
[32]  Liaw A, Wiener M (2002) Classification and regression by random forest. R News 2/3: 18–22.
[33]  Hastie T, Tibshirani R, Friedman J (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer. 745 p.
[34]  John G, Langley P (1995) Estimating continuous distributions in Bayesian classifier. San Mateo, CA: Morgan Kaufmann. 338–345 p.
[35]  Kursa M, Rudnicki W (2010) Feature selection with the Boruta Package. Journal of Statistical Software 36: 1–11.
[36]  Kohavi R, Sommer D (1995) Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology Heuristic Search. Montreal: Canada. 192–197 p.
[37]  Cohen J (1960) A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20: 37–46. doi: 10.1177/001316446002000104
[38]  McKenzie DP, Mackinnon AJ, Peladeau N, Onghena P, Bruce PC, et al. (1996) Comparing correlated kappas by resampling: Is one level of agreement significantly different from another? Journal of Psychiatric Research 30: 483–492. doi: 10.1016/s0022-3956(96)00033-7
[39]  Foody GM (2004) Thematic Map Comparison: Evaluating the Statistical Significance of Differences in Classification Accuracy. Photogrammetric Engineering & Remote Sensing 70: 627–633. doi: 10.14358/pers.70.5.627
[40]  Lüdtke A, Jerosch K, Herzog O, Schlüter M (2012) Development of a machine learning technique for automatic analysis of seafloor image data: Case example, Pogonophora coverage at mud volcanoes. Computers & Geosciences 39: 120–128. doi: 10.1016/j.cageo.2011.06.020
[41]  Li J, Heap AD, Potter A, Huang Z, Daniell JJ (2011) Can we improve the spatial predictions of seabed sediments? A case study of spatial interpolation of mud content across the southwest Australian margin. Continental Shelf Research 31: 1365–1376. doi: 10.1016/j.csr.2011.05.015
[42]  Kohavi R, John GH (1997) Wrappers for feature subset selection. Artificial Intelligence 97: 273–324. doi: 10.1016/s0004-3702(97)00043-x
[43]  Guyon I, Elisseeff A (2003) An Introduction to Variable and Feature Selection. Journal ofMachine Learning Research 3: 1157–1182.
[44]  Harrison R, Birchall R, Mann D, Wang W (2012) Novel consensus approaches to the reliable ranking of features for seabed imagery classification. International Journal of Neural Systems 22: 1250026. doi: 10.1142/s0129065712500268
[45]  Collier JS, Brown CJ (2005) Correlation of sidescan backscatter with grain size distribution of surficial seabed sediments. Marine Geology 214: 431–449. doi: 10.1016/j.margeo.2004.11.011
[46]  Fonseca L, Brown C, Calder B, Mayer L, Rzhanov Y (2009) Angular range analysis of acoustic themes from Stanton Banks Ireland: A link between visual interpretation and multibeam echosounder angular signatures. Applied Acoustics 70: 1298–1304. doi: 10.1016/j.apacoust.2008.09.008
[47]  Isaaks E, Srivastava R (1989) An introduction to applied geostatisitics. New York, Oxford: Oxford University Press. 561 p.
[48]  Pingree RD, Griffiths DK (1979) Sand transport paths around the British Isles resulting from M2 and M4 tidal interactions. Journal of the Marine Biological Association of the United Kingdom 59: 497–513. doi: 10.1017/s0025315400042806
[49]  Diesing M, Kubicki A, Winter C, Schwarzer K (2006) Decadal scale stability of sorted bedforms, German Bight, southeastern North Sea. Continental Shelf Research 26: 902–916. doi: 10.1016/j.csr.2006.02.009
[50]  Kubicki A, Diesing M (2006) Can old analogue sidescan sonar data still be useful? An example of a sonograph mosaic geo-coded by the DECCA navigation system. Continental Shelf Research 26: 1858–1867. doi: 10.1016/j.csr.2006.06.003
[51]  Last D (1992) The Accuracy and Coverage of Loran-C and of the Decca Navigator System - and the Fallacy of Fixed Errors. The Journal of Navigation 45: 36–51. doi: 10.1017/s0373463300010456
[52]  Foody GM, Boyd DS, Sanchez-Hernandez C (2007) Mapping a specific class with an ensemble of classifiers. International Journal of Remote Sensing 28: 1733–1746. doi: 10.1080/01431160600962566
[53]  Lundblad ER, Wright DJ, Miller J, Larkin EM, Rinehart R, et al. (2006) A Benthic Terrain Classification Scheme for American Samoa. Marine Geodesy 29: 89–111. doi: 10.1080/01490410600738021
[54]  Moran P (1950) Notes on continuous stochastic phenomena. Biometrika 37: 17–23. doi: 10.1093/biomet/37.1-2.17

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