935
An operational system for automatic school identification
on multibeam sonar echoes
Vasilis Trygonis, Stratis Georgakarakos, and E. John Simmonds
Trygonis, V., Georgakarakos, S., and Simmonds, E. J. 2009. An operational system for automatic school identification on multibeam sonar
echoes. – ICES Journal of Marine Science, 66: 935 – 949.
A system for identifying and tracking fish schools is demonstrated, based on the analysis of multibeam sonar data obtained by a
Simrad SP90 long-range sonar. Fish-school detection and identification techniques are similar to those commonly used for vertical
echosounders, further enhanced with innovative processing algorithms applied to successive multibeam echograms, increasing the
certainty that the identified objects are fish schools. Additionally, analysis of school dynamic parameters facilitates the classification
of targets into certain groups, here discriminating the fish aggregating device-natant fish complex from tuna. Statistical analysis of
selected tracks quantifies the spatio-temporal variability of the school descriptors, which are used retrospectively to select appropriate
analysis thresholds. The algorithms are implemented in an acquisition, visualization, and processing software platform that is flexible
regarding sonar characteristics (beam width and number of beams) and can be extended easily to track school echotraces in a threedimensional mode.
Keywords: multibeam sonar, school detection, school tracking, sonar software.
Received 20 August 2008; accepted 2 April 2009
V. Trygonis and S. Georgakarakos: Fisheries Management and Fisheries Acoustics Laboratory, Department of Marine Sciences, University of the
Aegean, University Hill, 81100 Mytilini, Greece. E. John Simmonds: Marine Laboratory, Victoria Road, Aberdeen AB11 9DB, UK. Correspondence
to S. Georgakarakos: tel: þ30 22510 36822; fax: þ30 22510 36809; e-mail: [email protected].
Introduction
Multibeam omnidirectional or sector-scanning sonars are gradually developing into realistic tools for the acoustic study of threedimensional morphology and visualization of schooling pelagic
species (Gerlotto et al., 2000, 2006; Melvin et al., 2002; Gerlotto
and Paramo, 2003; Paramo et al., 2007), schooling behaviour
(Pitcher et al., 1996; Misund et al., 2003), migration patterns
(Hafsteinsson and Misund, 1995), and vessel avoidance reactions
(Soria et al., 1996, 2003; Gerlotto et al., 2004).
Computerized systems for school detection and sizing came
into major use with the onset of the computer technology era in
the mid-1970s (Hewitt et al., 1976; Bodholt and Olsen, 1977).
Later technological advances facilitated the development of more
efficient systems for automatic detection and the measurement
of fish schools by multibeam sonars (Totland and Misund, 1993;
Misund et al., 1994). In general, multibeam data processing is performed via dedicated software tools (Lecornu et al., 1998; Mayer
et al., 1998; Melvin et al., 1998; Brehmer et al., 1999; Gerlotto
et al., 1999) because most available multibeam sonars are designed
for non-scientific operations, offering only visualization or limited
processing capabilities. Within these software tools, however, data
manipulation depends on laborious echogram scrutiny, highly
supervised selective storage and analysis of echogram images,
and the selection of appropriate segments of an echogram for
school isolation and the extraction of descriptors.
It is apparent that considerable progress in the overall multibeam acoustic methodology can be obtained by developing effective raw data acquisition and processing systems, which would
# 2009
implement robust algorithms for echogram analysis, school
detection, and extraction of descriptors, analogous to their
counterparts that are used in high-precision vertical echosounding. A general theoretical framework for the quantification of multibeam sonar measurements has been proposed (Cochrane et al.,
2003; Melvin et al., 2003), and innovative software tools have
been developed recently for semi-automated detection and threedimensional visualization of fish schools insonified with multibeam scanning sonars (Balabanian et al., 2007).
The extraction of quantitative descriptors is a prerequisite for
correct omnidirectional data interpretation, which can lead to a
deeper understanding of the behaviour of large pelagic species,
particularly in relation to the effects of fish aggregating devices
(FADs; Castro et al., 2002). Facilitating this need, the integrated
school-detection algorithm presented here allows for automatic
school isolation and extraction of quantitative descriptors in successive multibeam echograms, using raw beam data and insonification settings decoded from the Simrad SP90 sonar scientific
output. Tracking the detected schools in successive pings validates
the school identification process and produces a sequence of identified school traces generated from the same moving fish
aggregation.
School tracking through built-in sonar features (Hafsteinsson
and Misund, 1995) or software algorithms (Misund et al., 1994)
provides a continuous parameter description of several school
attributes, including aspects of their dynamics. A concurrent projection of vessel and school positions on the survey map improves
the presentation of sonar recordings, allowing for the
International Council for the Exploration of the Sea. Published by Oxford Journals. All rights reserved.
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936
V. Trygonis et al.
Table 1. Data telegrams contained in each binary file of the Simrad SP90 sonar scientific output.
Sequence
1
2
3
4
5
6
7
8
Telegram
Start of ping
Target
Trawl
Purse-seine
Ownship
Time data
Beam data
End of ping
Description
Insonification settings
User-monitored target(s)
Equipment
Equipment
Vessel dynamics
UTC timing
Acoustic raw data
Sonar and peripheral settings
reproduction of school movements either in absolute coordinates
or in relation to a vessel’s trajectory (Misund et al., 1998; Kvamme
et al., 2003; Brehmer et al., 2006).
The raw-data-processing algorithms presented here were developed within the European research project FADIO (Fish
Aggregating Devices as Instrumented Observatories of pelagic
ecosystems—EU Contract QLRI-CT-2002-02773; Dagorn et al.,
2006) to support multibeam acoustic research on tuna schools
around drifting FADs in the western Indian Ocean, using the
Simrad SP90 sonar. The methodology and its software implementation are not hardware-specific, so visualization, image processing, and the automatic school detection and tracking
algorithms are more generalized than those discussed above and
can be transferred to any other sonar system. These algorithms
work independently of transducer characteristics (beam width
and number of beams) and are valid for both two- and threedimensional backscattering arrays.
The software application was briefly reported in Brehmer et al.
(2007), mainly focusing on the overall sampling design of sonardata acquisition and the related hardware that was used within
the FADIO project. The objective of this manuscript is to describe
the processing system developed for multibeam raw-data analysis
in fisheries acoustics applications and to test its efficiency on
selected datasets from the FADIO cruises. The capabilities of the
system are demonstrated, and possible limitations and future
improvements are discussed.
Material and methods
Acoustic recordings of schools around drifting FADs were
acquired during five FADIO cruises, using the sampling methods
described in Brehmer et al. (2007). Following the visual scrutiny
and preliminary analysis of all FADIO multibeam raw data
around drifting FADs, a dataset of 15 well-documented records
was selected, comprising mainly survey data collected during
January, February, and October 2004. The duration of the multibeam data records varied between 20 min and 5 h. Only records
with constant sonar settings and, if possible, with the automatic
gain control (AGC) filter set to “off” were used to acquire absolute
measurements of Sv. The selected data covered a wide spectrum of
data characteristics, representative of different sonar ranges
(mainly 300 –900 m), instrument settings, and size of insonified
targets. The key features of the Simrad SP90 multibeam sonar
are an operational frequency of 26 kHz (range 20 –30 kHz in
steps of 1 kHz) and a theoretical horizontal range of 150–
8000 m. The cylindrical multi-element transducer provides a
3608 fan-shaped volume for each ping transmission, forming 64
beams on reception with a fixed along-beam digital resolution of
256 acoustic samples per beam. Each beam has 118 horizontal
and 98 vertical full angles between the 23 dB points. The acoustic
Example content
Insonification gains, tilt, sonar range
ID, Lat/Lon, depth
Distance from ship, bearing, width
Depth, length, sink rate
Lat/Lon, heading
UTC time-stamp
Beam data (colour-coded [0 . . . 63])
Gyro, inclinometer, system checks
beams can be tilted simultaneously between +108 and 2608 relative to the surface plane and are controlled by an electronic
beam-stabilization system that automatically compensates for
pitch and roll.
The SP90 sonar is equipped with a dedicated scientific output
and records one file per acoustic transmission, following a specific
binary-coded format (Anon., 2003). The binary file holds the
acoustic raw data, the sonar settings, and auxiliary information
from peripheral equipments interfaced to the sonar (GPS, gyrocompass, pitch, roll, vessel speed). These raw data files (*.dat)
are typically 17– 18 kB in size per ping and are stored automatically in a series of time-tagged file directories, each holding up
to 2 min of continuous data logging.
Each binary file typically contains the telegrams shown in
Table 1, which group related information into continuous datablocks. The beam data telegram takes up the biggest portion of
the file and holds the digitized backscatter for each acoustic
sample, colour-coded into 64 logarithmic scale integers [0 . . .
63]. During data retrieval, the processing software transforms
appropriately the beam data binary stream into a 256 64 array
(256 cells per beam), forming the final beam data matrix M for
each omnidirectional echogram.
According to the manufacturer, the SP90 raw beam data always
have a dynamic range of 30 dB and are expressed on a logarithmic
scale of 64 integers [0 . . . 63], where zero corresponds to the
weakest echo and 63 to the maximum value, with 30/64 0.5 dB steps. The two other gains affecting the scientific output,
the receiver gain GR and the display gain GD, change by 1 and
3 dB steps, respectively, and are provided in the “Start of ping”
telegram (Table 1). Note that for receiving absolute Sv measurements, the AGC sonar filter, which automatically adjusts the
gain in the preamplifiers according to the strength of the incoming
signals, must be disabled.
The recorded scientific output signal SvCS (colour scale) corresponds to the sum of the different amplification gains
SvCS ¼ GR þ GTVG þ GD þ Svm ;
ð1Þ
where GR, GTVG, GD, and Svm are, respectively, the receiver gain, the
time-varied gain (TVG), the display gain, and the measured
volume-backscattering strength before TVG amplification.
Assuming that the TVG function has been properly adjusted, the
back-transformation of the scientific output to the actual Sv
measurements follows the equation (all units in dB)
Sv ¼ SvCS ð30=64Þ GR 3GD :
ð2Þ
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System for automatic school identification on multibeam sonar echoes
Figure 1. Dataflow diagram of the MST software. Raw data files are imported into the MST, where telegrams are read in batch mode. The
visualization routines allow for echogram navigation and concurrent display of the cruise track according to the GPS telegrams, and statistical
parameters of the acoustic backscatter per ping are computed and displayed automatically. School detection and tracking are performed, and
the detection results are filed in the predefined school database. A sonar simulator submodule is available for post-processing corrections on
school-descriptor estimates.
Overview of data-processing tools
The school identification methodology was implemented in the
MATLABw high-level programming language, setting up a
stand-alone software platform, the “Multibeam Sonar Tracer”
(MST), that serves as a dedicated tool for raw data interpretation
and post-processing of school echoes recorded by multibeam horizontal sonars. Although its central specifications are shaped by
particular omnidirectional data interpretation needs, the software
contains all basic modules that are commonly found in fisheries
acoustic processing systems, as illustrated in the MST dataflow
diagram (Figure 1):
(ii) school geometric error estimations attributable to the beam
effect (Georgakarakos, 2005; Diner, 2007);
(iii) position and dynamic uncertainty estimations attributable to
the beam effect (Trygonis and Georgakarakos, 2007).
(i) acquisition routines, providing the interface for decoding
the acoustic raw data;
Utilization of these algorithms requires a systematic survey
sampling design and specific school-insonification strategies,
which may include (i) a temperature and salinity sampling grid,
providing sound velocity profiles for establishing the sound-ray
model, (ii) tilt-angle adjustments according to the typical depth
of the targeted fish species (Brehmer et al., 2007), and (iii) “drifting” and “prospecting” survey patterns (Brehmer et al., 2007), or
repeated measurements by gradually approaching the targeted
schools (Misund et al., 1995).
(ii) datafile management and echogram visualization or
animation;
The school-detection algorithm
(iii) multibeam echogram analysis tools;
(iv) school-detection routines, for extraction of two- or threedimensional school parameters;
(v) school tracking routines, for extraction of dynamic school
parameters;
(vi) tools for statistical analysis and presentation;
(vii) visualization of school tracks vs. ship and FAD tracks;
(viii) sonar simulation and ray-modelling tools.
The software design incorporates options for additional processing algorithms, such as:
(i) sound-ray calculations, implementing the Snell –Descartes
law (Lurton, 2002);
Echograms are typical examples of multiscale –multiresolution
images because each pixel represents an increasing volume with
distance from the transducer, so that both the geometric and
energetic descriptors of the insonified objects are affected continuously. In fisheries acoustics, this echogram singularity is
usually bypassed by considering the acoustic image as an algebraic array of arbitrary dimensions m n, regardless of the
sampling volume resolution (Georgakarakos and Paterakis,
1993; Reid and Simmonds, 1993; Weill et al., 1993; Barange,
1994; Diner, 2001). According to this principle, the schooldetection routine developed (SCHOOL) is designed to function
independently of the sonar characteristics that affect the echogram’s geometry, allowing the algorithms to run in three clearly
defined steps (Figure 2): (i) school detection, (ii) calculation of
school descriptors, and (iii) management of the SCHOOL
output information. This modular architecture of the system
938
V. Trygonis et al.
Figure 2. Flow diagram of the school-detection algorithm, which is compatible with both single- and multibeam acoustic data. For SP90 sonar
data, school detection is performed separately for each insonification matrix 256 64, and school descriptors are calculated after the
detection of schools has been performed over the whole array.
facilitates both the development process and future adaptations
to other sonar or echosounder devices. When applied on singlebeam echosounder data, where each ping gradually expands the
acoustic data matrix by one column, school detection can be performed in real time by dynamically updating the sample neighbouring relations and the extracted school descriptors, for each
new received sample. Regarding the scientific output of the
SP90, SCHOOL input parameters consist of selected sonar settings (observation range, spatial resolution/pulse duration, and
gain filters), platform navigational data, plus the beam data
matrix (M) per insonification.
School detection
The objective of the school-detection algorithm is to scan the
beam data matrix M and produce a labelled array L, equal in
dimensions to M, where all elements L(i, j) that belong to the
same school share the same unique identification tag. These
unique tags can then be used to calculate quantitative descriptors
for each school.
Two school-isolation techniques have been applied so far in
fisheries acoustics, namely “dilation –erosion” image processing
(Haralick et al., 1987; Reid and Simmonds, 1993) and the
pixel-by-pixel
“scrutiny
of
connectivity”
algorithm
(Georgakarakos and Paterakis, 1993; Totland and Misund, 1993;
Weill et al., 1993; Barange, 1994). The techniques are closely
related because in both cases the value of a pixel in the output
image is based on a comparison between the pixel’s neighbours.
Both techniques are well-documented and integrated in the
MATLABw Image Processing Toolbox as “dilation –erosion” and
“pixel-connectivity” algorithms (MATLAB, 2008). In the method
presented, however, instead of the built-in two-dimensional
“eight-connectivity” algorithm, a custom eight-connectivity
routine was developed in C, offering increased parameterization
and control over the general dataflow.
The first input parameter of the algorithm is the threshold
(Thrc) that removes unwanted echoes from further analysis by
setting M(i, j) ¼ 0. School detection and extraction also depend
on the spatial connectivity tolerances for acoustic samples,
defined as the maximum allowable distance between two
not-directly-connected samples belonging to the same school.
These neighbourhood tolerances, which are the next two userinput parameters, can be set independently for the along-beam
tolerance (TL) and cross-beam tolerance (TC), and measured
either in the number of acoustic samples or in metres (for TL)
and degrees (for TC).
During its execution, the algorithm scans the beam data array
M sample-by-sample in columns, starting from the top-left
sample M(1,1). For each sample M(i, j), if its value is larger than
the threshold [M(i, j) . Thrc], the two-dimensional spatial connectivity with the neighbouring samples is checked, according to
the user-defined TL and TC connectivity tolerances. The connectivity check is applied only to the samples that have already been
scanned and are “visible” to the algorithm, i.e. above and to the
left of the current sample M(i, j). However, SCHOOL re-calculates
sample connectivities if two initially separated school portions
merge into one at a later point.
939
System for automatic school identification on multibeam sonar echoes
Figure 3. Digitized school echogram with indications of selected descriptors that are extracted using the SCHOOL module. Grey scaling of
pixels corresponds to an energy scale. The size of each acoustic pixel is defined by the sampling units of the along- and cross-beam sample
sizes. All descriptors illustrated are defined in Table 2.
sample distance-ring s:
Calculation of school descriptors
Given that schools are detected by horizontal insonification, the
school parameters consider their horizontal features.
Nonetheless, they are defined and calculated similarly to those
measured by vertical echosounders (Reid, 2000). For each
school, a series of descriptors is calculated automatically, categorized into:
(i) metafile descriptors, providing information about the source
raw data file, vessel navigational data, and file management;
(ii) sonar descriptors, regarding the SP90 insonification settings;
(iii) input parameters for the SCHOOL algorithm;
(iv) morphometric, energetic, and positional school descriptors
calculated by the SCHOOL algorithm (Georgakarakos and
Paterakis, 1993; Reid et al., 2000).
The morphometric descriptors correspond to the twodimensional horizontal characteristics of the schools observed
(Figure 3), such as their along- and cross-beam dimensions,
shape, and horizontal area (Table 2). For each school detected,
the calculation of morphometric descriptors depends on two
intermediate parameters, the along-beam width, Lwb for beam b,
and the cross-beam width, Cws for sample distance-ring s:
Lwb ¼ Dr NSb
ðmÞ;
ð3Þ
where Dr (=sonar range/256) is the along-beam sample size (m)
and NSb the along-beam school width in beam b measured in a
number of acoustic samples, for all samples belonging to the
school, including empty samples or vacuoles (Fréon et al., 1992;
Gerlotto et al., 2006). The Cws parameter represents a chord
across all beams occupied by the school, separately for each
Cws ¼ 2 Dr Rs sin ðnb Du=2Þ
ðmÞ;
ð4Þ
where Rs is a positive integer ranging from 1 to 256, measuring in a
number of samples the distance between the transducer and the
active sample distance-ring s, nb the number of occupied beams
in distance-ring s, and Du the cross-beam sample size in degrees,
calculated as Du ¼ 3608/64 ¼ 5.6258.
A series of morphometric school descriptors is then calculated,
concerning the statistical characteristics (maximum, minimum,
and average) of the along-beam (Lw) and cross-beam (Cw)
dimensions of the school (Table 2). Further morphometric
descriptors are the number of echo samples belonging to the
school (ns), and the school’s area (A) in the horizontal plane of
the beam axes, which is estimated as the number of samples ns
making up the school, times the area of the sample in which the
school’s geometric centre (CG) resides.
Energetic school descriptors are calculated by first transforming
the volume-backscattering strength Sv(i) [Equation (2)] for the ith
school sample to a volume-backscattering coefficient (MacLennan
ðiÞ=10
et al., 2002) sv ðiÞ ¼ 10Sv ðm1 Þ. The school’s average volume
backscatter is then the average across all ns samples:
^sv ¼
ns
1X
sv ðiÞ ðm1 Þ;
ns i¼1
ð5Þ
and a number of energetic school descriptors is extracted, such as
the average acoustic density ave Sv, the maximum acoustic density
max Sv, the sum of acoustic volume backscatter Ssv, and the
within-school acoustic density variance var sv (Table 2).
Regarding positional descriptors, the school’s geometric centre
radial position relative to the transducer (average school range, RG)
940
V. Trygonis et al.
Table 2. Summary of selected school descriptors (details are given in text).
Variable
Definition
Metafile descriptors
Ping sequence ID
IDp
Vessel latitude
LatVL
LonVL
Vessel longitude
Vessel heading
HVL
Vessel speed
UVL
tUTC
UTC time
Sonar descriptors
R
Sonar observation range
T
Sonar tilt-angle
Receiver gain
GR
GTVG
Time-varied gain
AGC
GAGC
SCHOOL input parameters
IDs
School ID
Threshold
ThrC
TL
Along-beam tolerance
Cross-beam tolerance
TC
Morphometric descriptors
ns
Number of samples
Along-beam width per beam b
Lwb
Cws
Cross-beam width per distance-ring s
max Lw
Maximum along-beam width
min Lw
Minimum along-beam width
ave Lw
Average along-beam width
max Cw
Maximum cross-beam width
min Cw
Minimum cross-beam width
ave Cw
Average cross-beam width
A
Area
Energetic descriptors
Average acoustic density
ave Sv
Maximum acoustic density
max Sv
Unit
Formula
–
Degrees
Degrees
Degrees
m s21
hh:mm:ss:ms
–
–
–
–
–
–
m
Degrees
dB
dB
Off, weak, medium, strong
–
–
–
–
–
–
dB
samples or m
samples or degrees
–
–
–
–
–
m
m
m
m
m
m
m
m
m2
–
Lwb ¼ Dr NSb
Cws ¼ 2 Dr Rs sin(nb Du/2)
max Lw ¼ max(Lwb)
min Lw ¼ min(Lwb)
ave Lw ¼ mean(Lwb)
max Cw ¼ max(Cws)
min Cw ¼ min(Cws)
ave Cw ¼ mean(Cws)
A ¼ Dr Dum (CG) ns
dB
dB
Ssv
Sum of volume backscatter
m21
var sv
Variance of acoustic backscatter
–
ave Sv ¼ 10 log10ð^sv Þ
max Sv ¼ max(Sv(i))
Xns
Ssv ¼
s ðiÞ
i¼1 v
X
X2 varsv ¼ ns
s2v sv =½ns ðns 1Þ
m
RG ¼ Dr 1=ns
Positional descriptors
RG
School range
Xns
i¼1
RsðiÞ
QG
Average beam position
Degrees
QG ¼ uoffs þ Du ½0:5 þ 1=ns
RW
Weighted school range
m
RW ¼ Dr Pns
1
i¼1 sv ðiÞ
QW
Weighted beam position
Degrees
Xns
i¼1
Xns
i¼1
BeamðiÞ
RsðiÞ sv ðiÞ
QW ¼ uoffset þ Du ½0:5 þ Pns
1
Xns
i¼1
i¼1 sv ðiÞ
XG, YG
XW, YW
LatG
LonG
LatW
LonW
xy-distance to sonar
Weighted xy-distance to sonar
Geometric school latitude
Geometric school longitude
Weighted school latitude
Weighted school longitude
XG ¼ sin(HG) RG, YG ¼ cos(HG) RG
XW ¼ sin(HW) RW, YW ¼ cos(HW) RW
LatG ¼ LatVL þ YG/(60 1852)
LonG ¼ LonVL þ XG/[60 cos(LatVL) 1852]
LatW ¼ LatVL þ YW/(60 1852)
LonW ¼ LonVL þ XW/[60 cos(LatVL) 1852]
m
m
Degrees
Degrees
Degrees
Degrees
the SP90 resides:
is initially estimated:
ns
1X
Rs ðiÞ ðmÞ;
RG ¼ Dr
ns i¼1
BeamðiÞ sv ðiÞ
"
ð6Þ
as well as the school’s geometric centre angular position (average
beam position, QG) relative to the vessel’s bow, where beam#1 of
ns
1X
QG ¼ uoffset þ Du 0:5 þ
BeamðiÞ
ns i¼1
#
ðdegÞ;
ð7Þ
where uoffset is the sonar installation angular offset in degrees, and
Beam(i) is an integer counter corresponding to the beam numbers
941
System for automatic school identification on multibeam sonar echoes
Figure 4. Three-dimensional representation of the school-tracking procedure.
[1 . . . 64] that are covered by all ns school samples. Note that positive
direction for QG is anticlockwise, similar to the way that the SP90
beams are numbered (Figure 3).
The corresponding weighted descriptors (Table 2) that take
into account the volume backscatter of school samples are the
weighted school range (RW) and the weighted beam position
(QW). Subsequently, the geographic position of the school is estimated, combining the above descriptors with vessel navigational
data contained in the SP90 scientific output (gyrocompass vessel
heading HVL and GPS coordinates LatVL and LonVL, all units in
degrees). These parameters are referenced to different coordinate
systems, requiring some intermediate conversions before the
final descriptor computation. The school’s geometric centre
heading (HG) in degrees relative to north is
HG ¼
HVL QG ;
HVL QG þ 360;
if HG 0
if HG , 0
ðdegÞ;
ð8Þ
and the distance in metres of the school’s geometric centre along
the Cartesian x- and y-axes is XG ¼ sin(HG) RG, and YG ¼
cos(HG) RG, where the positive x- and y-axis direction is east
and north, respectively, vessel-centred. As 1 m in the x-direction
is equivalent to 1/[60 cos(LatVL) 1852] degrees of longitude
and 1 m in the y-direction is 1/(60 1852) degrees of latitude,
the geographic position of the school’s geometric centre (LonG,
LatG) can be computed (Table 2). In a similar way, using the
weighted RW and QW quantities, the corresponding weighted
descriptors XW, YW, LonW, and LatW are extracted.
The final stage in the algorithm execution is the compilation of
all descriptors and the creation of the ASCII-formatted SCHOOL
output file (*.csv). For each school detected, a single line contains
the quantitative descriptors calculated above, plus additional
metafile information regarding the sonar settings and detectionalgorithm configuration.
School-tracking module
Tracking schools in successive multibeam echograms is analogous
to the widely used procedure of tracking single fish in consecutive
pings from vertical echosounding (Brede et al., 1990; Ona and
Hansen, 1991). Instead of identifying single fish echoes, the multibeam echograms are scanned for certain two-dimensional school
shapes that have comparable geometric and dynamic features
between successive insonifications (Figure 4). Hence, the tracking
module integrated in MST can be considered an extension of fishtracking algorithms, where the positions, areas, and energetic features of successive schools are compared, applying appropriate
ping-to-ping matching criteria.
In this context, school tracking is approached as a common
region-matching problem for discrete-time sequences of image
frames. Standard approaches to tracking object motion can be
roughly classified into those using gradient models (Brockett,
1990) or correspondence of motion tokens (Ullman, 1979). The
latter models are more immune to noise and robust to both
short- and long-range motion (Fuh and Maragos, 1996).
The school-tracking algorithm developed in MST is guided by
Ullman’s (1979) correspondence principles and functions as an extension of the school-detection algorithm for isolating specific targets
and following their trajectory and spatio-temporal characteristics.
The general dataflow of the tracking algorithm is outlined below.
Feature detection
This process includes the identification and extraction of quantitative descriptors for all schools observed in a dataset of multibeam echograms, and is facilitated by the SCHOOL algorithm.
All information required for tracking purposes is extracted exclusively from the SCHOOL output file, without any further use of
the SP90 raw data telegrams.
Configuration and definition of tracking criteria
By importing a standard SCHOOL output file from the analytical
database, the tracking module automatically scans it, separates
942
internally each insonification, retrieves all necessary sonar settings,
and calculates descriptive statistics for the number of schools per
ping, as well as their distribution according to their size and distance from the transducer. The purposes of this preprocessing
function are to facilitate the appropriate configuration of the
tracking algorithm and to serve as a stand-alone preliminary
analysis of the SCHOOL file.
The user can parameterize the available school cut-off filters
optionally, e.g. exclude acoustic targets very close to the transducer
or small objects. These preliminary cut-off filters work as a simple
threshold applied on echotrace area and/or distance from sonar,
excluding unwanted targets (e.g. reverberation) from further processing, speeding up the execution time of the tracking algorithm.
The next step in the configuration process is the definition of
the ping-to-ping school-matching criteria. Specifically, if Si and
Sj are two identified schools, isolated in two consecutive multibeam echograms, the algorithm tests if Sj and Si are the echotraces
from the same fish aggregation observed at time intervals tj and ti,
respectively. Fixing school Si, the system considers school Sj as a
possible candidate to match with Si, if it passes three criteria
successfully:
(i) centroid distance: their distance should be less than an upper
bound expressed in pixels or metres (L);
(ii) area difference: the area of matching schools should not vary
extensively jA(Si) 2 A(Sj)j , aA(Si), where 0 , a , 1;
(iii) density difference: the average acoustic densities of the
two schools should not vary extensively jave Sv(Si) 2
ave Sv(Sj)j , d ave Sv(Si).
The parameters L, a, and d are control settings for the correspondence process. Specifically, L in the sonar application is divided
into two components for along-beam samples (La) and crossbeam samples (Lc). Therefore, school Sj may be matched with
school Si only if its centroid lies inside the 2La 2Lc window
centred at the centroid of Si. No smoothing is initially applied
on the school positions, and the user defines the control settings
following a preliminary analysis of the data. Alternatively,
“optimal” settings can be estimated by simulation, where the performance of different post-tracking smoothing filters is evaluated
(Trygonis and Georgakarakos, 2007), appropriately parameterized
according to the particular characteristics (i.e. sonar range/
resolution and target trajectory) of the track. Further aspects of
this choice are explained in the discussion.
Gating and data association
Gating is the process in which the differences between candidate
schools Sj and Si are calculated, and if they are above the control
settings, the hypothesis that they belong to successive observations
of a particular school is rejected. In the current software
implementation, the centroid, area, and density parameters used
for tracking are treated separately by the gate. Several alternative
specifications exist in the literature; see e.g. Handegard et al.
(2005) for an application on split-beam data or Blackman and
Popoli (1999) for a detailed review.
The next step in the process is data association, which is the
pairing of successive echotraces observed at time intervals tj and
ti; only candidates that have passed through the gate are considered
here. The objective is the rejection of false candidate pairs, and the
successful association of valid ones into tracks. In the particular
application on tuna schools, it is typically a single candidate Sj
V. Trygonis et al.
that passes the gate, because the usually large school size forbids
overlap in the feature space. However, if more than one school
candidates in ping tj compete over the same observation in ping
ti, the school Sj with the closest observations in the feature space
to Si is associated, and further comparisons cease for the particular
track, in that particular ping.
Track maintenance and export
The subroutine facilitates the circular creation, validation, and termination of school tracks, running in parallel with the
data-association algorithm. When an observation fails to match
an existing track, a new track object is initiated, and the
ping-to-ping scanning procedure is repeated for all pings in the
active dataset. On completion, the tracking outcome is displayed
graphically to help validate the quality of the tracking analysis.
As a final step, the results are exported to ASCII files on
demand, after the user has defined the minimum track length
(measured in a number of consecutive pings) for a school to be
successfully considered as “tracked”. Note that the results for
tracked schools and untracked targets are stored in separate files
with identical format to facilitate comparison.
For each output category, a summary table is stored with the
main descriptive statistics of each school (area, distance from
sonar, track length, etc.), plus the instantaneous speed (Brehmer
et al., 2006) of the track (displacement vector divided by the
ping interval). Two additional columns provide: (i) a smoothed
estimate of the average school speed, after application of the
appropriate smoother for the particular track (depending on
sonar range and trajectory type) that is decided through simulation (Trygonis and Georgakarakos, 2007), and (ii) the school’s
movement straightness index, as defined by Misund (1992). Part
of the default output is an automatically generated post-tracking
session report, documenting all user and software settings,
SCHOOL source file information, and descriptive summary of
the session results.
Software implementation and application
All aforementioned algorithms are implemented in the MST and
are interactively controlled through the main graphical user interface (GUI) of the software (Figure 5). Central to the main GUI
window is the active multibeam echogram presented in bow-up
mode by default, as well as the colour map of the acoustic backscatter. Further data visualization is supported by a threedimensional echogram submodule that allows for a precomputed
animation of successive insonifications, based on the theoretical
geometric characteristics of the SP90 sampling volume. The multibeam echogram window is an interactive surface in which selected
sample characteristics (acoustic density, distance to transducer,
angular position, georeferenced coordinates) are displayed by
hovering the mouse pointer over the echogram, whereas various
supportive tools are provided for interactive distance or area
measurements and annotation purposes. A special sector-selection
tool is available for defining and isolating particular regions of the
echogram, either for custom analysis focusing exclusively on the
encircled regions or for exclusion from further processing according to need, e.g. for isolating the vessel’s wake manually. These
custom regions can be configured to apply automatically on the
whole echogram sequence, so that the vessel’s wake removal, for
instance, does not slow the overall school-detection procedure.
The right panel of the GUI hosts the echogram quicknavigation tools, selected indicators of vessel parameters, the
943
System for automatic school identification on multibeam sonar echoes
Figure 5. The main GUI of the MST software.
sonar insonification settings, and the control panel of the
SCHOOL algorithm. Statistical parameters of the total acoustic
backscatter per insonification are displayed dynamically on the
left part of the GUI, specifically a colour map indexed histogram
of Sv and a scatterplot of sample volume backscatter against
sample range.
For school detection, the user navigates through successive
echograms and configures the algorithm accordingly; school
detection is performed per ping, and the detection results are
filed in the SCHOOL database. The detection output is also displayed on the active echogram by annotating each school detected
with a unique tag over its geometric centre. These school tags
remain interactive throughout the analysis, providing the user
with descriptor information in the particular ping. For exploratory
analysis, school detection can be shown only on the echogram
window.
Results
To investigate the effect of the volume-backscattering strength (Sv)
threshold value on the school identification procedure (Trygonis,
2009), three datasets with identical sonar settings were processed.
Each dataset consisted of 50 consecutive pings, on which school
detection was repeatedly performed with varying threshold, covering the complete 30-dB range of the SP90, in a stepwise manner.
SCHOOL was configured to run separately on a region containing
exclusively the fish school observed and on the rest of the echogram that contained randomly scattered acoustic targets, which
we refer to as “noise”. The results are illustrated in Figure 6,
showing that using a 249.0 dB threshold, a small portion
(10%) of the school trace is removed, whereas acoustic
samples characterized as “noise” are reduced by 90%. Note that
these results were consistent across all three datasets.
Tracking results
For the entire raw data record processed, the application of a relatively high threshold of 250.0 to 249.0 dB and all sample connectivity tolerances set to zero resulted in 1900 tracked traces (i.e.
school sequences, or fragments of a particular school sequence),
which represent a small portion (,10%) of the total acoustic
traces encountered. An example is portrayed in Figure 7, which
represents a 10-min dataset of length, where the large number of
isolated acoustic traces before tracking (n ¼ 3752) was reduced
by 93% (n ¼ 287).
Comparing the total area of initially encountered with tracked
traces in Figure 7, it is clear that the latter represents .90% of the
total encountered echotrace area per echogram. Similarly, the
tracked traces (n ¼ 1900) that resulted in the datasets analysed
represented .90 and .95% of the total encountered areas and
total volume-backscattering, respectively.
944
V. Trygonis et al.
Figure 6. Effect of the applied volume-backscattering strength threshold on school-detection characteristics. nsthr is the number of acoustic
samples per threshold level, and nso is the number of acoustic samples in the original echogram. Each plotted point represents the ratio of
removed echogram samples relative to their initial number, averaged over all 50 pings per dataset; shaded regions represent the 95%
confidence interval of the mean, calculated after bootstrapping.
The boxplots of echotrace characteristics in successfully tracked
vs. rejected targets revealed important differences between the two
sets (Figure 8). As expected, the tracking algorithm accepted the
relatively larger schools with stronger backscatter and fairly constant spatio-temporal features, successfully discriminating them
from small, randomly scattered acoustic targets (false echoes,
reverberation, or even loosely aggregated fish) that featured
limited or no temporal continuity.
The visualization of isolated school trajectories and the statistical analysis of their descriptors allowed classification of their
dynamics concerning kinetic, geometric, and energetic variability.
Certain moving objects were traced and classified as a particular
group according to their energetic and morphometric descriptors
(Figure 9, left). In all cases (n ¼ 7) where sufficient FAD georeferenced data or accurate survey-log information were available, the
FAD tracks were recognized as belonging to this specific group,
whose boxplots revealed a statistically significant difference from
tracked echotraces identified as schools. Obviously, the measured
acoustic density of the FAD tracks included both the backscattered
energy from the submerged part of the FAD and that from the
FAD-natant fish. The measured school descriptors of the
FAD-natant fish complex revealed a temporally more robust behaviour, with less variability in its area and shape geometry
(Figure 9, right).
Discussion
Horizontal insonification suffers from water stratification and
surface reverberation, and it is not always easy to model sound
propagation or transmission loss, especially far from the transducer. Such limitations directed the industry towards developing
sonars serving as fish- or target-finders, and not as dedicated
equipment for quantitative measurements, at least up to recently.
Most acoustic laboratories were therefore obliged either to use
sonars solely for qualitative behavioural observation or to
develop dedicated software for signal-acquisition and postprocessing (Brehmer et al., 2006). The system presented here
provides tools for echogram visualization and automatic school
detection and tracking on multibeam-sonar raw data. The algorithms are interactive, so system settings for school isolation or
tracking can be adjusted after preliminary data analysis.
Fishers operating tuna fish-finders distinguish fish schools from
noise, following ping-by-ping the school traces that remain on the
sonar screen and show “tuna-like” behaviour, using their accumulated empirical knowledge (Moreno et al., 2007). The tracking
algorithm developed imitated this human identification approach,
using numerical data to replace the more ad hoc approach, producing a series of “tracked traces”, representing the motion of the fish
schools observed. Statistical analysis of fish-school characteristics
(two-dimensional and dynamic descriptors) of carefully selected
tracks revealed the spatio-temporal variability of descriptors,
which retrospectively guide the user to select the appropriate processing thresholds. Despite the sense of subjectivity in this retrospective procedure, all methods applied until now for identifying
and tracking single fish targets or fish aggregations have been
based on some critical thresholds defined empirically. For instance,
the identification of single-fish echoes is based on specific duration,
amplitude, and phase-stability limits, or other criteria defined after
exploratory analysis (Ona and Barange, 1999). A review of the
history of single-fish tracking, with particular focus on detections
using multibeam sonar, is given by Schell and Jaffe (2004).
System for automatic school identification on multibeam sonar echoes
945
Figure 7. Histograms of detected schools according to their size and distance to the transducer (a) before and (b) after tracking. The small
image at the top right is a typical echogram from the dataset, featuring a clearly defined school at 200 m from the sonar.
It is very important, however, to underline the differences
between single-fish and fish-school tracking, in terms of equipment, detection algorithms, calculations of descriptors, and their
statistics. In single-fish echoes, the angular positions of the
target, as measured with split-beam technology, are sufficient for
tracking, whereas this is not the case for conventional multibeam
sonar; consequently, related split-beam de-biasing techniques
cannot be applied (Ehrenberg and Torkelson, 1996; Demer et al.,
1999; Xie, 2000). Moreover, for single-target tracking, the echoes
are related mainly to fish-orientation angle and body size,
whereas in fish-school tracking, the successive echoes are
additionally affected by the multiple stochastic dynamics of the
malleable fish-aggregation structure.
Obviously, the precision and accuracy of position measurements that characterize the split-beam technology are incomparable with the low performance of current multibeam sonar,
particularly long-range devices. However, tracking schools with
multibeam sonar has certain similarities to single-target tracking,
as applied to echosounder data; both procedures accept or reject
the candidate backscatterer by comparing the deviations of its
characteristics with the rest of the observations. An analysis of
larger datasets and the utilization of simulation approaches
946
V. Trygonis et al.
Figure 8. Boxplots of morphometric, energetic, and positional descriptors for tracked and non-tracked echotraces. The minimum acceptable
track length was set to ten consecutive pings (ntracked ¼ 660 echotraces, forming nine school tracks; nnon-tracked ¼ 1805 echotraces, forming
291 rejected tracks).
(Trygonis and Georgakarakos, 2007) can improve our understanding of the spatio-temporal variability of school descriptors and
support the selection of tracking settings. As for single-fish tracking, not all echotraces have the same probability of being selected,
and the mean size of the targets is overestimated. This bias is
range-dependent and requires Monte Carlo simulations for bias
correction, similar to those developed for single targets
(Ehrenberg and Torkelson, 1996).
It is also known that, even in vertical insonification, school
descriptors are biased by the beam effect, and simulation
approaches can provide a means for acoustic descriptor correction
(Diner, 2001, 2007; Georgakarakos, 2005). For equivalent reasons,
this bias is also unavoidable in multibeam sonar measurements,
which are usually carried out with relatively wide beam widths
(.58) and at long sonar ranges. Analogous simulations in
three dimensions that take into account the more complicated
conditions of multibeam horizontal sonars are needed for
morphometric and energetic corrections.
In the cases we tested, the schools tracked were 10% of the
acoustic traces isolated, but measuring the tracked schools as a percentage of the total area or fish abundance per echogram, they represented .90 and .95% of the total, respectively. How much of
the remaining backscattering is caused by noise, very small fish
echoes, or low-density fish aggregations is unclear. Comparative
studies, taking into account different sea conditions and scanning
with varying tilt-angles, could provide some insight into this
question.
Until now, the tracking procedure alone was used as the definitive criterion for accepting a trace sequence as a tracked school.
However, school-tracking analysis on larger datasets, including
species information, could provide auxiliary covariates for
improving predictions. Consequently, more advanced discrimination techniques can be used, probably reducing the aforementioned 10 and 5% uncertainty in the total area and fish abundance.
Most techniques developed for tracking manoeuvrable targets
can also be applied to reliable fish-school tracking in multibeam
echograms. In the past (Nøttestad et al., 1996; Brehmer et al.,
2006), fish velocity was calculated by differencing the noisy position measurements, although it is known that this method generates bias and great uncertainty (Mulligan and Chen, 2000). As an
alternative, standard Kalman filters (KF) have been used
(Maybeck, 1979), or various KF improvements such as the
extended KF (Anderson and Moore, 1979) and the unscented
KF, which all, however, assume a Gaussian distribution (Julier
and Uhlmann, 1996). More advanced improvements combine
KF algorithms with neural nets (Lobbia et al., 1998; Blackman
and Popoli, 1999).
Nonetheless, fish tracking is not a trivial problem; among other
difficulties, fish behaviour, which researchers do try to investigate,
is in itself an important input parameter for the models that needs
to be developed (Schell et al., 2004). The solution is a data-driven
approach to tracking, such as the segmenting track identifier of
Schell et al. (2004). We are currently working on developing a
similar simulated-data-driven approach, which can estimate the
947
System for automatic school identification on multibeam sonar echoes
Figure 9. Dynamic behaviour of two acoustic targets, the drifting FAD, and an associated school. (a) Target trajectories while the vessel is
drifting near the FAD. (b) Radial distance of the FAD and its associated school from the transducer. The associated school shows a higher
dynamic (continuous line). (c– e) Boxplots of the average acoustic density, school area, and along- to cross-beam dimension ratio.
posterior distribution of school kinematics. The data required are
generated from a three-dimensional school-tracking simulator
(Trygonis, 2009).
Statistically analysing and plotting the tracked school positions
and descriptors, some “density stable” objects with more robust
acoustic characteristics were isolated, which were identified as
the drifting FADs and the associated natant fish (i.e. the
FAD-natant fish complex), confirmed by the recorded FAD positions or survey-log data. Fish species associated with FADs are
classified according to their distance to the floating object into
different groups (Fréon and Dagorn, 2000). In our measurements, the intra-/extranatant species, according to the terminology used, remain close to the FAD and below the SP90 sonar
resolution, whereas the circumnatant species are free-swimming
at a distance of 50 –200 m, loosely associated with the drifting
object.
The current data-acquisition module was adjusted to read the
binary output of the SP90 multibeam sonar, but the code can be
modified easily to receive the output of any other sonar device,
particularly as the school identification and tracking algorithms
were designed to work independently of transducer characteristics
(beam width and number of beams). The utilization of the next
generation of sonars, with narrow-beam angles (minimum 2.28)
and reduced side-lobes, such as the newly developed ME70, is
expected to provide more accurate measurements, especially for
small or low-density schools (Trenkel et al., 2008).
Acknowledgements
We thank our partners in the project FADIO, Patrice Brehmer,
Erwan Josse, Gala Moreno, and John Dalen, for their helpful suggestions and comments on software requirements. The development was supported financially by the EU FADIO programme.
Reviewers Nils Olav Handegard and Mathieu Doray are thanked
for their extensive and valued comments on the manuscript.
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