Seabed classification using SBES data
Peter Hung (NUIM), Seán McLoone (NUIM), Xavier Monteys (GSI)
Background
Seabed type clustering
Identification of seabed type (such as mud, sand and rock) is of value in
many applications including seabed mapping, coastal management and
seabed conservation. There are two main types of echo sounders for such
purposes, multi-beam (MBES) and single-beam (SBES). The difference
between the two types of echo sounders is that while SBES has one
transceiver that emits and detects echo time series at normal incidence to
the seabed, MBES has multiple transceivers sending sound waves at
various angles towards the seabed. Usually, seabed clustering is
performed from data collected by MBES during sea survey trips while
single-beam echo sounders (SBES) are usually employed for the
measurement of bathymetry (depth). However, it is believed that SBES
time series data also contains useful information for seabed type analysis
due to its ability to achieve deeper penetration of the seabed substrate.
Feature extraction
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.
180
Ground truthing
All existing clustering approaches require extensive ground truthing (such
as grabbing, coring and visual inspection) which are not practical in deep
waters. By adopting a statistical approach, the objective is to perform
multi-frequency clustering as an alternative to extensive ground truthing
prior to label validation. In this study, features from three frequencies (12,
38, 200 kHz) are collected, spatially filtered and normalised before
clustering.
12 kHz
38 kHz
200 kHz
140
120
Above seabed (m)
100
5m
80
2.2 m
60
12 kHz
38 kHz
200 kHz
25
Mean Adj. MSE
160
Mean Adj. MSE
Challenges of SBES clustering
• Influence of environmental factors (salinity, sea water temperature,
bathymetry, slope);
• Characteristics of SBES echo which are frequency-dependent;
• Operational limitations (automatic gain control, ship movement).
30
20
Below seabed (m)
9.2 m
15
10
1.3 m
40
2.1 m
5
20
0
0
0.5
1
1.5
2
2.5
3
3.5
Above seabed (m)
4
4.5
5
0
0
5
10
15
20
25
Below seabed (m)
Fig. 5 Optimal depth selection from mean adjacent mean-square-err
Clustering
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
Fig. 1 Single-beam echo sounder
Fig. 2 Transceiver and data
capture equipment
Clustering on features derived from optimally selected intervals yields
more distinctive and continuous (and hence geologically plausible)
clusters than if the full time-series intervals are employed.
Preliminary results suggest that the best performance is obtained by
employing QT local clustering with the optimally selected intervals.
Methodology
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.
20
Echo return (dB)
0
-20
Water
column
-40
Future work:
• Improving the computation and memory efficiency of the QT local and
MSC clustering algorithms
• Determination of an optimum feature set from SBES time-series for
seabed classification
Optimal depth
Maximal depth
Optimal depth
Maximal depth
PCA + k-means
Region of
Interest
Peak
Amplitude
Overall, data fusion using multi-frequency SBES data improves
information richness and data quality control improves the reliability of
clustering results and provides a more geologically realistic interpretation
of the survey area.
Seabed
substrate
-60
-80
A
-100
0
50
100
150
B
200
250
Distance from sea level (m)
Fig. 4 SBES time series
Seabed detection
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.
QT local
Fig. 3 Data processing flowchart
Best
result
Fig. 6 Clustering results from optimal and maximal depths
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.