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

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  • David Stephens
  • Markus Diesing

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.

Suggested Citation

  • David Stephens & Markus Diesing, 2014. "A Comparison of Supervised Classification Methods for the Prediction of Substrate Type Using Multibeam Acoustic and Legacy Grain-Size Data," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-14, April.
  • Handle: RePEc:plo:pone00:0093950
    DOI: 10.1371/journal.pone.0093950
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    References listed on IDEAS

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    Cited by:

    1. Jin Li & Maggie Tran & Justy Siwabessy, 2016. "Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-29, February.
    2. Asma Shaheen & Javed Iqbal, 2018. "Spatial Distribution and Mobility Assessment of Carcinogenic Heavy Metals in Soil Profiles Using Geostatistics and Random Forest, Boruta Algorithm," Sustainability, MDPI, vol. 10(3), pages 1-20, March.
    3. Fatemeh Tajik & Mingzheng Wang & Xiaohui Zhang & Jie Han, 2020. "Evaluation of the impact of body mass index on venous thromboembolism risk factors," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-17, July.

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