20. Region of Interest. 0. Peak Amplitude. Water column. Seabed substrate. -20. Echo return (dB). -40. Above seabed (m). Below seabed (m). -60. 9.2 m. 5 m. 2.2 m. -80. A. B. 1.3 m. 2.1 m. -100. 0. 50. 100. 150. 200. 250. Distance from sea level (m).
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Echo return (dB)
Above seabed (m)
Below seabed (m)
Distance from sea level (m)
Fig. 5 Optimal depth selection from mean adjacent mean-square-err
Fig. 1 Single-beam echo sounder Fig. 2 Transceiver and data capture equipment
PCA + k-means
Fig. 3 Data processing flowchart Fig. 4 SBES time series
Fig. 6 Clustering results from optimal and maximal depths
Seabed classification using SBES data
Peter Hung (NUIM), Seán McLoone (NUIM), Xavier Monteys (GSI)
Due to the high dimensionality of time series data, the large number of samples collected in sea trips and the heteroscedastic noise contained within, a total of four features are extracted from the raw echo data. The temporal mean and associated standard deviation are standard features that has direct relationships with seabed geology. To convey information about the relationship between adjacent time series, measures of spatial randomness and spatial correlation are proposed.
Optimal depth selection
A novel adjacent mean-square-error metric is used to estimate the optimum time series interval for feature extraction. This is based on the assumption that seabed substrate should produce gradually changing features unless the echo captures additional information, such as sidelobe backscatter and background noise. Determined separately for the above and below time series segments, optimal depth is defined as the point of inflexion or minimum in the following plots.
PCA with k-means is the industry standard for clustering seabed data. However, it is less effective at dealing with non-Gaussian clusters and requires the number of clusters to be specified a priori. In this work extensions of quality thresholding approaches, including local quality thresholding (QT local) and max-separation clustering (MSC) are developed. These have several advantages over k-means, including automatic cluster number determination and robustness to outliers.
Results and future work
Semi-automatic statistical approach
To achieve consistent and reliable classification, each processing step needs to be carefully assessed. Since SBES data contains less geological information and redundancy for quality assurance compared to MBES data, additional measures need to be taken to ensure the quality of the raw data. The pre-processing stage involves further expert inspection for data integrity, initial ‘cleanup’ to mitigate the effects of systematic deviations of sonar measurements and spatial sampling to improve the signal-to-noise ratio.
Two approaches are employed. The first assumes the seabed is located at the peak amplitude of each echo time series return. The second method estimates the location of the seabed by spatially smoothing the bathymetry using a second-order Butterworth filter. Utilising both bathymetric approaches allows the detection of ‘bad’ data samples.
Research presented in this poster was funded by a Strategic Research Cluster Grant (07/SRC/I1168) by Science Foundation Ireland under the National Development Plan. The authors gratefully acknowledge this support.