2009 ICES Trygonis Et Al 935

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An operational system for automatic school identicationon 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 identication on multibeam sonarechoes. ICES Journal of Marine Science, 66: 935949.

A system for identifying and tracking sh schools is demonstrated, based on the analysis of multibeam sonar data obtained by aSimrad SP90 long-range sonar. Fish-school detection and identication techniques are similar to those commonly used for verticalechosounders, further enhanced with innovative processing algorithms applied to successive multibeam echograms, increasing thecertainty that the identied objects are sh schools. Additionally, analysis of school dynamic parameters facilitates the classicationof targets into certain groups, here discriminating the sh aggregating device-natant sh complex from tuna. Statistical analysis of selected tracks quanties the spatio-temporal variability of the school descriptors, which are used retrospectively to select appropriateanalysis thresholds. The algorithms are implemented in an acquisition, visualization, and processing software platform that is exibleregarding sonar characteristics (beam width and number of beams) and can be extended easily to track school echotraces in a three-dimensional 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. Correspondenceto S. Georgakarakos: tel: 30 22510 36822; fax: 30 22510 36809; e-mail: [email protected].

IntroductionMultibeam omnidirectional or sector-scanning sonars are gradu-ally developing into realistic tools for the acoustic study of three-dimensional morphology and visualization of schooling pelagicspecies (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 cameinto major use with the onset of the computer technology era inthe mid-1970s (Hewitt et al ., 1976; Bodholt and Olsen, 1977).Later technological advances facilitated the development of moreefcient systems for automatic detection and the measurementof sh schools by multibeam sonars (Totland and Misund, 1993;Misund et al ., 1994). In general, multibeam data processing is per-formed via dedicated software tools (Lecornu et al ., 1998; Mayeret al ., 1998; Melvin et al ., 1998; Brehmer et al ., 1999; Gerlottoet al ., 1999) because most available multibeam sonars are designed

for non-scientic operations, offering only visualization or limitedprocessing capabilities. Within these software tools, however, datamanipulation depends on laborious echogram scrutiny, highly supervised selective storage and analysis of echogram images,and the selection of appropriate segments of an echogram forschool isolation and the extraction of descriptors.

It is apparent that considerable progress in the overall multi-beam acoustic methodology can be obtained by developing effec-tive raw data acquisition and processing systems, which would

implement robust algorithms for echogram analysis, schooldetection, and extraction of descriptors, analogous to theircounterparts that are used in high-precision vertical echosound-ing. A general theoretical framework for the quantication of mul-tibeam sonar measurements has been proposed (Cochrane et al .,2003; Melvin et al ., 2003), and innovative software tools havebeen developed recently for semi-automated detection and three-dimensional visualization of sh schools insonied with multi-beam scanning sonars (Balabanian et al ., 2007).

The extraction of quantitative descriptors is a prerequisite forcorrect omnidirectional data interpretation, which can lead to adeeper understanding of the behaviour of large pelagic species,particularly in relation to the effects of sh aggregating devices(FADs; Castro et al ., 2002). Facilitating this need, the integratedschool-detection algorithm presented here allows for automaticschool isolation and extraction of quantitative descriptors in suc-cessive multibeam echograms, using raw beam data and insoni-cation settings decoded from the Simrad SP90 sonar scienticoutput. Tracking the detected schools in successive pings validates

the school identication process and produces a sequence of ident-ied school traces generated from the same moving shaggregation.

School tracking through built-in sonar features (Hafsteinssonand Misund, 1995) or software algorithms (Misund et al ., 1994)provides a continuous parameter description of several schoolattributes, including aspects of their dynamics. A concurrent projection of vessel and school positions on the survey map improvesthe presentation of sonar recordings, allowing for the

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reproduction of school movements either in absolute coordinatesor in relation to a vessels trajectory (Misund et al ., 1998; Kvammeet al ., 2003; Brehmer et al ., 2006).

The raw-data-processing algorithms presented here were devel-oped within the European research project FADIO (FishAggregating Devices as Instrumented Observatories of pelagicecosystemsEU Contract QLRI-CT-2002-02773; Dagorn et al .,2006) to support multibeam acoustic research on tuna schoolsaround drifting FADs in the western Indian Ocean, using the

Simrad SP90 sonar. The methodology and its software implemen-tation are not hardware-specic, so visualization, image proces-sing, and the automatic school detection and trackingalgorithms are more generalized than those discussed above andcan be transferred to any other sonar system. These algorithmswork independently of transducer characteristics (beam widthand number of beams) and are valid for both two- and three-dimensional backscattering arrays.

The software application was briey reported in Brehmer et al .(2007), mainly focusing on the overall sampling design of sonar-data acquisition and the related hardware that was used withinthe FADIO project. The objective of this manuscript is to describethe processing system developed for multibeam raw-data analysisin sheries acoustics applications and to test its efciency on

selected datasets from the FADIO cruises. The capabilities of thesystem are demonstrated, and possible limitations and futureimprovements are discussed.

Material and methodsAcoustic recordings of schools around drifting FADs wereacquired during ve FADIO cruises, using the sampling methodsdescribed in Brehmer et al . (2007). Following the visual scrutiny and preliminary analysis of all FADIO multibeam raw dataaround drifting FADs, a dataset of 15 well-documented recordswas selected, comprising mainly survey data collected duringJanuary, February, and October 2004. The duration of the multi-beam data records varied between 20 min and 5 h. Only recordswith constant sonar settings and, if possible, with the automatic

gain control (AGC) lter set to off were used to acquire absolutemeasurements of Sv . The selected data covered a wide spectrum of data characteristics, representative of different sonar ranges(mainly 300900 m), instrument settings, and size of insoniedtargets. The key features of the Simrad SP90 multibeam sonarare an operational frequency of 26 kHz (range 20 30 kHz insteps of 1 kHz) and a theoretical horizontal range of 1508000 m. The cylindrical multi-element transducer provides a3608 fan-shaped volume for each ping transmission, forming 64beams on reception with a xed along-beam digital resolution of 256 acoustic samples per beam. Each beam has 11 8 horizontaland 9 8 vertical full angles between the 2 3 dB points. The acoustic

beams can be tilted simultaneously between +10 8 and 2 608 rela-tive to the surface plane and are controlled by an electronicbeam-stabilization system that automatically compensates forpitch and roll.

The SP90 sonar is equipped with a dedicated scientic outputand records one le per acoustic transmission, following a specicbinary-coded format (Anon., 2003). The binary le holds theacoustic raw data, the sonar settings, and auxiliary informationfrom peripheral equipments interfaced to the sonar (GPS, gyro-

compass, pitch, roll, vessel speed). These raw data les (*. dat )are typically 1718 kB in size per ping and are stored automati-cally in a series of time-tagged le directories, each holding upto 2 min of continuous data logging.

Each binary le typically contains the telegrams shown inTable 1, which group related information into continuous data-blocks. The beam data telegram takes up the biggest portion of the le and holds the digitized backscatter for each acousticsample, colour-coded into 64 logarithmic scale integers [0 . . .63]. During data retrieval, the processing software transformsappropriately the beam data binary stream into a 256 64 array (256 cells per beam), forming the nal beam data matrix M foreach omnidirectional echogram.

According to the manufacturer, the SP90 raw beam data always

have a dynamic range of 30 dB and are expressed on a logarithmicscale of 64 integers [0 . . . 63], where zero corresponds to theweakest echo and 63 to the maximum value, with 30 / 64 %0.5 dB steps. The two other gains affecting the scientic output,the receiver gain GR and the display gain GD , change by 1 and3 dB steps, respectively, and are provided in the Start of pingtelegram (Table 1). Note that for receiving absolute Sv measure-ments, the AGC sonar lter, which automatically adjusts thegain in the preampliers according to the strength of the incomingsignals, must be disabled.

The recorded scientic output signal Sv CS (colour scale) corre-sponds to the sum of the different amplication gains

Sv CS GR GTVG GD Sv m ; 1

where GR , GTVG , GD , and Sv m are, respectively, the receiver gain, thetime-varied gain (TVG), the display gain, and the measuredvolume-backscattering strength before TVG amplication.Assuming that the TVG function has been properly adjusted, theback-transformation of the scientic output to the actual Sv measurements follows the equation (all units in dB)

Sv Sv CS30=64 GR 3GD: 2

Table 1. Data telegrams contained in each binary le of the Simrad SP90 sonar scientic output.

Sequence Telegram Description Example content1 Start of ping Insonication settings Insonication gains, tilt, sonar range2 Target User-monitored target(s) ID, Lat/ Lon, depth3 Trawl Equipment Distance from ship, bearing, width4 Purse-seine Equipment Depth, length, sink rate5 Ownship Vessel dynamics Lat/ Lon, heading

6 Time data UTC timing UTC time-stamp7 Beam data Acoustic raw data Beam data (colour-coded [0 . . . 63])8 End of ping Sonar and peripheral settings Gyro, inclinometer, system checks

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Overview of data-processing toolsThe school identication methodology was implemented in theMATLABw high-level programming language, setting up astand-alone software platform, the Multibeam Sonar Tracer(MST), that serves as a dedicated tool for raw data interpretationand post-processing of school echoes recorded by multibeam hori-

zontal sonars. Although its central specications are shaped by particular omnidirectional data interpretation needs, the softwarecontains all basic modules that are commonly found in sheriesacoustic processing systems, as illustrated in the MST dataow diagram (Figure 1):

(i) acquisition routines, providing the interface for decodingthe acoustic raw data;

(ii) datale management and echogram visualization oranimation;

(iii) multibeam echogram analysis tools;

(iv) school-detection routines, for extraction of two- or three-dimensional school parameters;

(v) school tracking routines, for extraction of dynamic schoolparameters;

(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 proces-sing algorithms, such as:

(i) sound-ray calculations, implementing the SnellDescarteslaw (Lurton, 2002);

(ii) school geometric error estimations attributable to the beameffect (Georgakarakos, 2005; Diner, 2007);

(iii) position and dynamic uncertainty estimations attributable tothe beam effect (Trygonis and Georgakarakos, 2007).

Utilization of these algorithms requires a systematic survey sampling design and specic school-insonication strategies,which may include (i) a temperature and salinity sampling grid,providing sound velocity proles for establishing the sound-ray model, (ii) tilt-angle adjustments according to the typical depthof the targeted sh species (Brehmer et al ., 2007), and (iii) drift-ing and prospecting survey patterns (Brehmer et al ., 2007), orrepeated measurements by gradually approaching the targetedschools (Misund et al ., 1995).

The school-detection algorithmEchograms are typical examples of multiscale multiresolutionimages because each pixel represents an increasing volume withdistance from the transducer, so that both the geometric andenergetic descriptors of the insonied objects are affected con-

tinuously. In sheries acoustics, this echogram singularity isusually bypassed by considering the acoustic image as an alge-braic array of arbitrary dimensions m n, regardless of thesampling volume resolution (Georgakarakos and Paterakis,1993; Reid and Simmonds, 1993; Weill et al ., 1993; Barange,1994; Diner, 2001). According to this principle, the school-detection routine developed (SCHOOL) is designed to functionindependently of the sonar characteristics that affect the echo-grams geometry, allowing the algorithms to run in three clearly dened steps (Figure 2): (i) school detection, (ii) calculation of school descriptors, and (iii) management of the SCHOOLoutput information. This modular architecture of the system

Figure 1. Dataow diagram of the MST software. Raw data les are imported into the MST, where telegrams are read in batch mode. Thevisualization routines allow for echogram navigation and concurrent display of the cruise track according to the GPS telegrams, and statisticalparameters of the acoustic backscatter per ping are computed and displayed automatically. School detection and tracking are performed, andthe detection results are led in the predened school database. A sonar simulator submodule is available for post-processing corrections onschool-descriptor estimates.

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facilitates both the development process and future adaptationsto other sonar or echosounder devices. When applied on single-beam echosounder data, where each ping gradually expands the

acoustic data matrix by one column, school detection can be per-formed in real time by dynamically updating the sample neigh-bouring relations and the extracted school descriptors, for eachnew received sample. Regarding the scientic output of theSP90, SCHOOL input parameters consist of selected sonar set-tings (observation range, spatial resolution / pulse duration, andgain lters), platform navigational data, plus the beam datamatrix ( M ) per insonication.

School detectionThe objective of the school-detection algorithm is to scan thebeam data matrix M and produce a labelled array L , equal indimensions to M , where all elements L(i, j ) that belong to thesame school share the same unique identication tag. These

unique tags can then be used to calculate quantitative descriptorsfor each school.

Two school-isolation techniques have been applied so far insheries acoustics, namely dilationerosion image processing(Haralick et al ., 1987; Reid and Simmonds, 1993) and thepixel-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 outputimage is based on a comparison between the pixels neighbours.Both techniques are well-documented and integrated in the

MATLABw Image Processing Toolbox as dilationerosion andpixel-connectivity algorithms (MATLAB, 2008). In the methodpresented, however, instead of the built-in two-dimensional

eight-connectivity algorithm, a custom eight-connectivity routine was developed in C, offering increased parameterizationand control over the general dataow.

The rst input parameter of the algorithm is the threshold(Thr c ) that removes unwanted echoes from further analysis by setting M (i, j ) 0. School detection and extraction also dependon the spatial connectivity tolerances for acoustic samples,dened as the maximum allowable distance between twonot-directly-connected samples belonging to the same school.These neighbourhood tolerances, which are the next two user-input parameters, can be set independently for the along-beamtolerance ( T L ) and cross-beam tolerance ( T C ), and measuredeither in the number of acoustic samples or in metres (for T L )and degrees (for T C ).

During its execution, the algorithm scans the beam data array M sample-by-sample in columns, starting from the top-leftsample M (1,1). For each sample M (i, j ), if its value is larger thanthe threshold [ M (i, j ) . Thr c ], the two-dimensional spatial con-nectivity with the neighbouring samples is checked, according tothe user-dened T L and T C connectivity tolerances. The connec-tivity check is applied only to the samples that have already beenscanned and are visible to the algorithm, i.e. above and to theleft of the current sample M (i, j ). However, SCHOOL re-calculatessample connectivities if two initially separated school portionsmerge into one at a later point.

Figure 2. Flow diagram of the school-detection algorithm, which is compatible with both single- and multibeam acoustic data. For SP90 sonardata, school detection is performed separately for each insonication matrix 256 64, and school descriptors are calculated after thedetection of schools has been performed over the whole array.



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Calculation of school descriptorsGiven that schools are detected by horizontal insonication, theschool parameters consider their horizontal features.Nonetheless, they are dened and calculated similarly to thosemeasured by vertical echosounders (Reid, 2000). For eachschool, a series of descriptors is calculated automatically, categor-ized into:

(i) metale descriptors, providing information about the sourceraw data le, vessel navigational data, and le management;

(ii) sonar descriptors, regarding the SP90 insonication settings;

(iii) input parameters for the SCHOOL algorithm;

(iv) morphometric, energetic, and positional school descriptorscalculated by the SCHOOL algorithm (Georgakarakos andPaterakis, 1993; Reid et al ., 2000).

The morphometric descriptors correspond to the two-dimensional 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, Lw b for beam b,and the cross-beam width, Cw s for sample distance-ring s:

Lw b Dr NSb m; 3

where Dr (=sonar range / 256) is the along-beam sample size (m)and NSb the along-beam school width in beam b measured in anumber of acoustic samples, for all samples belonging to theschool, including empty samples or vacuoles (Fre on et al ., 1992;Gerlotto et al ., 2006). The Cw s parameter represents a chordacross all beams occupied by the school, separately for each

sample distance-ring s:

Cw s 2 Dr Rs sin nb Du =2 m; 4

where Rs is a positive integer ranging from 1 to 256, measuring in anumber of samples the distance between the transducer and theactive sample distance-ring s, nb the number of occupied beamsin distance-ring s, and Du the cross-beam sample size in degrees,calculated as Du 3608 / 64 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 morphometricdescriptors are the number of echo samples belonging to theschool (ns), and the schools area ( A) in the horizontal plane of the beam axes, which is estimated as the number of samples nsmaking up the school, times the area of the sample in which theschools geometric centre ( C G ) resides.

Energetic school descriptors are calculated by rst transformingthe volume-backscattering strength Sv (i) [Equation (2)] for the ithschool sample to a volume-backscattering coefcient (MacLennanet al ., 2002) sv i 10S

i=10v m1. The schools average volume

backscatter is then the average across all ns samples:

sv 1nsX

ns

i1sv i m1; 5

and a number of energetic school descriptors is extracted, such asthe average acoustic density ave Sv , the maximum acoustic density max Sv , the sum of acoustic volume backscatter S sv , and thewithin-school acoustic density variance var sv (Table 2).

Regarding positional descriptors, the schools geometric centreradial position relative to the transducer (average school range, RG )

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 dened by the sampling units of the along- and cross-beam samplesizes. All descriptors illustrated are dened in Table 2.



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is initially estimated:

RG Dr 1nsX

ns

i1Rsi m; 6

as well as the schools geometric centre angular position (averagebeam position, Q G ) relative to the vessels bow, where beam#1 of

the SP90 resides:

Q G u offset Du 0:5 1nsX

ns

i1Beami" #deg; 7

where u offset is the sonar installation angular offset in degrees, andBeam(i) is an integer counter corresponding to the beam numbers

Table 2. Summary of selected school descriptors (details are given in text).

Variable Denition Unit FormulaMetale descriptors

IDp Ping sequence ID LatVL Vessel latitude Degrees LonVL Vessel longitude Degrees HVL Vessel heading Degrees

UVL Vessel speed m s2 1

t UTC UTC time hh:mm:ss:ms Sonar descriptors

R Sonar observation range m T Sonar tilt-angle Degrees GR Receiver gain dB GTVG Time-varied gain dB GAGC AGC Off, weak, medium, strong

SCHOOL input parametersIDs School ID ThrC Threshold dB T L Along-beam tolerance samples or m T C Cross-beam tolerance samples or degrees

Morphometric descriptorsns Number of samples Lw b Along-beam width per beam b m Lw b Dr NSbCw s Cross-beam width per distance-ring s m Cw s 2 Dr Rs sin(nb Du / 2)max Lw Maximum along-beam width m max Lw max(Lw b )min Lw Minimum along-beam width m min Lw min(Lw b )ave Lw Average along-beam width m ave Lw mean(Lw b )max Cw Maximum cross-beam width m max Cw max(Cw s)min Cw Minimum cross-beam width m min Cw min(Cw s)ave Cw Average cross-beam width m ave Cw mean(Cw s) A Area m2 A Dr Du m (C G) ns

Energetic descriptorsave Sv Average acoustic density dB ave Sv 10 log10svmax Sv Maximum acoustic density dB max Sv max(Sv(i))S sv Sum of volume backscatter m

2 1 S sv Xns

i1svi

var sv Variance of acoustic backscatter varsv ns

Xs2v

X2

sv =nsns 1

Positional descriptorsRG School range m RG Dr 1=ns X

ns

i1Rsi

Q G Average beam position Degrees Q G u offs Du 0:5 1=nsXns

i1Beami

RW Weighted school range m RW Dr 1

Pnsi1 sviX

ns

i1Rsisvi

Q W Weighted beam position Degrees Q W u offset Du 0:5 1

Pnsi1 sviX

ns

i1Beamisvi

X G , Y G xy-distance to sonar m X G sin(HG) RG , Y G cos(HG) RG X W , Y W Weighted xy-distance to sonar m X W sin(HW) RW , Y W cos(HW) RWLatG Geometric school latitude Degrees Lat G LatVL Y G/ (60 1852)LonG Geometric school longitude Degrees Lon G LonVL X G/ [60 cos(LatVL) 1852]LatW Weighted school latitude Degrees Lat W LatVL Y W/ (60 1852)LonW Weighted school longitude Degrees Lon W LonVL X W/ [60 cos(LatVL) 1852]



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[1 . . . 64] that are covered byall ns school samples. Note that positivedirection for Q G is anticlockwise, similar to the way that the SP90beams are numbered (Figure 3).

The corresponding weighted descriptors (Table 2) that takeinto account the volume backscatter of school samples are theweighted school range ( RW ) and the weighted beam position(Q W ). Subsequently, the geographic position of the school is esti-mated, combining the above descriptors with vessel navigationaldata contained in the SP90 scientic output (gyrocompass vesselheading H VL and GPS coordinates Lat VL and Lon VL , all units in

degrees). These parameters are referenced to different coordinatesystems, requiring some intermediate conversions before thenal descriptor computation. The schools geometric centreheading ( H G ) in degrees relative to north is

H G H VL Q G; if H G ! 0

H VL Q G 360; if H G , 0deg; 8

and the distance in metres of the schools geometric centre alongthe Cartesian x - and y-axes is X G sin(H G ) RG , and Y G cos(H G ) RG , where the positive x - and y -axis direction is eastand north, respectively, vessel-centred. As 1 m in the x -direction

is equivalent to 1 / [60 cos(LatVL ) 1852] degrees of longitudeand 1 m in the y -direction is 1 / (60 1852) degrees of latitude,the geographic position of the schools geometric centre (Lon G ,LatG ) can be computed (Table 2). In a similar way, using theweighted RW and Q W quantities, the corresponding weighteddescriptors X W , Y W , LonW , and LatW are extracted.

The nal stage in the algorithm execution is the compilation of all descriptors and the creation of the ASCII-formatted SCHOOLoutput le (*. csv ). For each school detected, a single line containsthe quantitative descriptors calculated above, plus additionalmetale information regarding the sonar settings and detection-algorithm conguration.

School-tracking moduleTracking schools in successive multibeam echograms is analogousto the widely used procedure of tracking single sh in consecutivepings from vertical echosounding (Brede et al ., 1990; Ona andHansen, 1991). Instead of identifying single sh echoes, the multi-beam echograms are scanned for certain two-dimensional schoolshapes that have comparable geometric and dynamic featuresbetween successive insonications (Figure 4). Hence, the trackingmodule integrated in MST can be considered an extension of sh-tracking algorithms, where the positions, areas, and energetic fea-

tures of successive schools are compared, applying appropriateping-to-ping matching criteria.In this context, school tracking is approached as a common

region-matching problem for discrete-time sequences of imageframes. Standard approaches to tracking object motion can beroughly classied into those using gradient models (Brockett,1990) or correspondence of motion tokens (Ullman, 1979). Thelatter models are more immune to noise and robust to bothshort- and long-range motion (Fuh and Maragos, 1996).

The school-tracking algorithm developed in MST is guided by Ullmans (1979) correspondenceprinciplesand functionsas anexten-sion of the school-detection algorithm for isolating specic targetsand following their trajectory and spatio-temporal characteristics.The general dataow of the tracking algorithm is outlined below.

Feature detectionThis process includes the identication and extraction of quanti-tative descriptors for all schools observed in a dataset of multi-beam echograms, and is facilitated by the SCHOOL algorithm.All information required for tracking purposes is extracted exclu-sively from the SCHOOL output le, without any further use of the SP90 raw data telegrams.

Conguration and denition of tracking criteriaBy importing a standard SCHOOL output le from the analyticaldatabase, the tracking module automatically scans it, separates

Figure 4. Three-dimensional representation of the school-tracking procedure.



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internally each insonication, retrieves all necessary sonar settings,and calculates descriptive statistics for the number of schools perping, as well as their distribution according to their size and dis-tance from the transducer. The purposes of this preprocessingfunction are to facilitate the appropriate conguration of thetracking algorithm and to serve as a stand-alone preliminary analysis of the SCHOOL le.

The user can parameterize the available school cut-off ltersoptionally, e.g. exclude acoustic targets very close to the transduceror small objects. These preliminary cut-off lters work as a simplethreshold applied on echotrace area and / or distance from sonar,excluding unwanted targets (e.g. reverberation) from further pro-cessing, speeding up the execution time of the tracking algorithm.

The next step in the conguration process is the denition of the ping-to-ping school-matching criteria. Specically, if Si andS j are two identied schools, isolated in two consecutive multi-beam echograms, the algorithm tests if S j and Si are the echotracesfrom the same sh aggregation observed at time intervals t j and t i,respectively. Fixing school Si, the system considers school S j as apossible candidate to match with Si, if it passes three criteriasuccessfully:

(i) centroid distance: their distance should be less than an upperbound expressed in pixels or metres ( L);

(ii) area difference: the area of matching schools should not vary extensively j A(Si) 2 A(S j )j , a A(Si), where 0 , a , 1;

(iii) density difference: the average acoustic densities of thetwo schools should not vary extensively jave Sv (Si) 2ave Sv (S j )j , d ave Sv (Si).

The parameters L, a, and d are control settings for the correspon-dence process. Specically, L in the sonar application is dividedinto two components for along-beam samples ( La ) and cross-beam samples ( Lc ). Therefore, school S j may be matched withschool Si only if its centroid lies inside the 2 La 2Lc window

centred at the centroid of Si. No smoothing is initially appliedon the school positions, and the user denes the control settingsfollowing a preliminary analysis of the data. Alternatively,optimal settings can be estimated by simulation, where the per-formance of different post-tracking smoothing lters is evaluated(Trygonis and Georgakarakos, 2007), appropriately parameterizedaccording 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 associationGating is the process in which the differences between candidateschools S j and Si are calculated, and if they are above the controlsettings, the hypothesis that they belong to successive observations

of a particular school is rejected. In the current softwareimplementation, the centroid, area, and density parameters usedfor tracking are treated separately by the gate. Several alternativespecications exist in the literature; see e.g. Handegard et al .(2005) for an application on split-beam data or Blackman andPopoli (1999) for a detailed review.

The next step in the process is data association, which is thepairing of successive echotraces observed at time intervals t j andt i; only candidates that have passed through the gate are consideredhere. The objective is the rejection of false candidate pairs, and thesuccessful association of valid ones into tracks. In the particularapplication on tuna schools, it is typically a single candidate S j

that passes the gate, because the usually large school size forbidsoverlap in the feature space. However, if more than one schoolcandidates in ping t j compete over the same observation in pingt i, the school S j with the closest observations in the feature spaceto Si is associated, and further comparisons cease for the particulartrack, in that particular ping.

Track maintenance and export The subroutine facilitates the circular creation, validation, and ter-mination of school tracks, running in parallel with thedata-association algorithm. When an observation fails to matchan existing track, a new track object is initiated, and theping-to-ping scanning procedure is repeated for all pings in theactive dataset. On completion, the tracking outcome is displayedgraphically to help validate the quality of the tracking analysis.

As a nal step, the results are exported to ASCII les ondemand, after the user has dened the minimum track length(measured in a number of consecutive pings) for a school to besuccessfully considered as tracked. Note that the results fortracked schools and untracked targets are stored in separate leswith identical format to facilitate comparison.

For each output category, a summary table is stored with themain descriptive statistics of each school (area, distance fromsonar, track length, etc.), plus the instantaneous speed (Brehmeret al ., 2006) of the track (displacement vector divided by theping interval). Two additional columns provide: (i) a smoothedestimate of the average school speed, after application of theappropriate smoother for the particular track (depending onsonar range and trajectory type) that is decided through simu-lation (Trygonis and Georgakarakos, 2007), and (ii) the schoolsmovement straightness index, as dened by Misund (1992). Partof the default output is an automatically generated post-trackingsession report, documenting all user and software settings,SCHOOL source le information, and descriptive summary of the session results.

Software implementation and applicationAll aforementioned algorithms are implemented in the MST andare interactively controlled through the main graphical user inter-face (GUI) of the software (Figure 5). Central to the main GUIwindow is the active multibeam echogram presented in bow-upmode by default, as well as the colour map of the acoustic back-scatter. Further data visualization is supported by a three-dimensional echogram submodule that allows for a precomputedanimation of successive insonications, based on the theoreticalgeometric characteristics of the SP90 sampling volume. The multi-beam echogram window is an interactive surface in which selectedsample characteristics (acoustic density, distance to transducer,angular position, georeferenced coordinates) are displayed by

hovering the mouse pointer over the echogram, whereas varioussupportive tools are provided for interactive distance or areameasurements and annotation purposes. A special sector-selectiontool is available for dening and isolating particular regions of theechogram, either for custom analysis focusing exclusively on theencircled regions or for exclusion from further processing accord-ing to need, e.g. for isolating the vessels wake manually. Thesecustom regions can be congured to apply automatically on thewhole echogram sequence, so that the vessels wake removal, forinstance, does not slow the overall school-detection procedure.

The right panel of the GUI hosts the echogram quick-navigation tools, selected indicators of vessel parameters, the



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sonar insonication settings, and the control panel of theSCHOOL algorithm. Statistical parameters of the total acousticbackscatter per insonication are displayed dynamically on theleft part of the GUI, specically a colour map indexed histogramof Sv and a scatterplot of sample volume backscatter againstsample range.

For school detection, the user navigates through successiveechograms and congures the algorithm accordingly; schooldetection is performed per ping, and the detection results areled in the SCHOOL database. The detection output is also dis-played on the active echogram by annotating each school detectedwith a unique tag over its geometric centre. These school tagsremain 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 echogramwindow.

ResultsTo investigate the effect of the volume-backscattering strength ( Sv )threshold value on the school identication procedure (Trygonis,2009), three datasets with identical sonar settings were processed.Each dataset consisted of 50 consecutive pings, on which schooldetection was repeatedly performed with varying threshold, cover-ing the complete 30-dB range of the SP90, in a stepwise manner.SCHOOL was congured to run separately on a region containing

exclusively the sh school observed and on the rest of the echo-gram that contained randomly scattered acoustic targets, whichwe refer to as noise. The results are illustrated in Figure 6,showing that using a 2 49.0 dB threshold, a small portion( 10%) of the school trace is removed, whereas acousticsamples characterized as noise are reduced by 90%. Note thatthese results were consistent across all three datasets.

Tracking resultsFor the entire raw data record processed, the application of a rela-tively high threshold of 2 50.0 to 2 49.0 dB and all sample connec-tivity 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 acoustictraces encountered. An example is portrayed in Figure 7, whichrepresents a 10-min dataset of length, where the large number of isolated acoustic traces before tracking ( n 3752) was reducedby 93% (n 287).

Comparing the total area of initially encountered with trackedtraces in Figure 7, it is clear that the latter represents . 90% of thetotal encountered echotrace area per echogram. Similarly, thetracked traces ( n 1900) that resulted in the datasets analysedrepresented . 90 and . 95% of the total encountered areas andtotal volume-backscattering, respectively.

Figure 5. The main GUI of the MST software.



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The boxplots of echotrace characteristics in successfully trackedvs. rejected targets revealed important differences between the twosets (Figure 8). As expected, the tracking algorithm accepted therelatively larger schools with stronger backscatter and fairly con-

stant spatio-temporal features, successfully discriminating themfrom small, randomly scattered acoustic targets (false echoes,reverberation, or even loosely aggregated sh) that featuredlimited or no temporal continuity.

The visualization of isolated school trajectories and the statisti-cal analysis of their descriptors allowed classication of theirdynamics concerning kinetic, geometric, and energetic variability.Certain moving objects were traced and classied as a particulargroup according to their energetic and morphometric descriptors(Figure 9, left). In all cases (n 7) where sufcient FAD georefer-enced data or accurate survey-log information were available, theFAD tracks were recognized as belonging to this specic group,whose boxplots revealed a statistically signicant difference fromtracked echotraces identied as schools. Obviously, the measured

acoustic density of the FAD tracks included both the backscatteredenergy from the submerged part of the FAD and that from theFAD-natant sh. The measured school descriptors of theFAD-natant sh complex revealed a temporally more robust be-haviour, with less variability in its area and shape geometry (Figure 9, right).

DiscussionHorizontal insonication suffers from water stratication andsurface reverberation, and it is not always easy to model soundpropagation or transmission loss, especially far from the transdu-cer. Such limitations directed the industry towards developing

sonars serving as sh- or target-nders, and not as dedicatedequipment for quantitative measurements, at least up to recently.Most acoustic laboratories were therefore obliged either to usesonars solely for qualitative behavioural observation or to

develop dedicated software for signal-acquisition and post-processing (Brehmer et al ., 2006). The system presented hereprovides tools for echogram visualization and automatic schooldetection and tracking on multibeam-sonar raw data. The algor-ithms are interactive, so system settings for school isolation ortracking can be adjusted after preliminary data analysis.

Fishers operating tunash-nders distinguish sh schools fromnoise, following ping-by-ping the school traces that remain on thesonar screen and show tuna-like behaviour, using their accumu-lated empirical knowledge (Moreno et al ., 2007). The trackingalgorithm developed imitated this human identication approach,using numerical data to replace the more ad hoc approach, produ-cing a series of tracked traces, representing the motion of the shschools observed. Statistical analysis of sh-school characteristics

(two-dimensional and dynamic descriptors) of carefully selectedtracks revealed the spatio-temporal variability of descriptors,which retrospectively guide the user to select the appropriate pro-cessing thresholds. Despite the sense of subjectivity in this retro-spective procedure, all methods applied until now for identifyingand tracking single sh targets or sh aggregations have beenbased on some critical thresholds dened empirically. For instance,the identication of single-sh echoesis based on specic duration,amplitude, and phase-stability limits, or other criteria dened afterexploratory analysis (Ona and Barange, 1999). A review of thehistory of single-sh tracking, with particular focus on detectionsusing multibeam sonar, is given by Schell and Jaffe (2004).

Figure 6. Effect of the applied volume-backscattering strength threshold on school-detection characteristics. nsthr is the number of acousticsamples 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%condence interval of the mean, calculated after bootstrapping.



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It is very important, however, to underline the differencesbetween single-sh and sh-school tracking, in terms of equip-ment, detection algorithms, calculations of descriptors, and theirstatistics. In single-sh echoes, the angular positions of thetarget, as measured with split-beam technology, are sufcient fortracking, whereas this is not the case for conventional multibeamsonar; consequently, related split-beam de-biasing techniquescannot be applied (Ehrenberg and Torkelson, 1996; Demer et al .,1999; Xie, 2000). Moreover, for single-target tracking, the echoesare related mainly to sh-orientation angle and body size,whereas in sh-school tracking, the successive echoes are

additionally affected by the multiple stochastic dynamics of themalleable sh-aggregation structure.

Obviously, the precision and accuracy of position measure-ments that characterize the split-beam technology are incompar-able with the low performance of current multibeam sonar,particularly long-range devices. However, tracking schools withmultibeam sonar has certain similarities to single-target tracking,as applied to echosounder data; both procedures accept or rejectthe candidate backscatterer by comparing the deviations of itscharacteristics with the rest of the observations. An analysis of larger datasets and the utilization of simulation approaches

Figure 7. Histograms of detected schools according to their size and distance to the transducer (a) before and (b) after tracking. The smallimage at the top right is a typical echogram from the dataset, featuring a clearly dened school at 200 m from the sonar.



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(Trygonis and Georgakarakos, 2007) can improve our understand-

ing of the spatio-temporal variability of school descriptors andsupport the selection of tracking settings. As for single-sh track-ing, not all echotraces have the same probability of being selected,and the mean size of the targets is overestimated. This bias isrange-dependent and requires Monte Carlo simulations for biascorrection, similar to those developed for single targets(Ehrenberg and Torkelson, 1996).

It is also known that, even in vertical insonication, schooldescriptors are biased by the beam effect, and simulationapproaches 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 complicatedconditions of multibeam horizontal sonars are needed formorphometric and energetic corrections.

In the cases we tested, the schools tracked were 10% of theacoustic traces isolated, but measuring the tracked schools as a per-centage of the total area or sh abundance per echogram, they rep-resented . 90 and . 95% of the total, respectively. How much of the remaining backscattering is caused by noise, very small shechoes, or low-density sh aggregations is unclear. Comparativestudies, taking into account different sea conditions and scanningwith varying tilt-angles, could provide some insight into thisquestion.

Until now, the tracking procedure alone was used as the deni-

tive criterion for accepting a trace sequence as a tracked school.However, school-tracking analysis on larger datasets, includingspecies information, could provide auxiliary covariates forimproving predictions. Consequently, more advanced discrimi-nation techniques can be used, probably reducing the aforemen-tioned 10 and 5% uncertainty in the total area and sh abundance.

Most techniques developed for tracking manoeuvrable targetscan also be applied to reliable sh-school tracking in multibeamechograms. In the past (Nttestad et al ., 1996; Brehmer et al .,2006), sh velocity was calculated by differencing the noisy pos-ition measurements, although it is known that this method gener-ates bias and great uncertainty (Mulligan and Chen, 2000). As analternative, standard Kalman lters (KF) have been used(Maybeck, 1979), or various KF improvements such as the

extended KF (Anderson and Moore, 1979) and the unscentedKF, which all, however, assume a Gaussian distribution (Julierand Uhlmann, 1996). More advanced improvements combineKF algorithms with neural nets (Lobbia et al ., 1998; Blackmanand Popoli, 1999).

Nonetheless, sh tracking is not a trivial problem; among otherdifculties, sh behaviour, which researchers do try to investigate,is in itself an important input parameter for the models that needsto be developed (Schell et al ., 2004). The solution is a data-drivenapproach to tracking, such as the segmenting track identier of Schell et al . (2004). We are currently working on developing asimilar simulated-data-driven approach, which can estimate the

Figure 8. Boxplots of morphometric, energetic, and positional descriptors for tracked and non-tracked echotraces. The minimum acceptabletrack length was set to ten consecutive pings ( ntracked 660 echotraces, forming nine school tracks; nnon-tracked 1805 echotraces, forming291 rejected tracks).



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posterior distribution of school kinematics. The data required aregenerated from a three-dimensional school-tracking simulator(Trygonis, 2009).

Statistically analysing and plotting the tracked school positionsand descriptors, some density stable objects with more robustacoustic characteristics were isolated, which were identied asthe drifting FADs and the associated natant sh (i.e. theFAD-natant sh complex), conrmed by the recorded FAD pos-itions or survey-log data. Fish species associated with FADs areclassied according to their distance to the oating object intodifferent groups (Fre on and Dagorn, 2000). In our measure-ments, the intra- / extranatant species, according to the terminol-ogy used, remain close to the FAD and below the SP90 sonarresolution, whereas the circumnatant species are free-swimming

at a distance of 50200 m, loosely associated with the driftingobject.The current data-acquisition module was adjusted to read the

binary output of the SP90 multibeam sonar, but the code can bemodied easily to receive the output of any other sonar device,particularly as the school identication and tracking algorithmswere designed to work independently of transducer characteristics(beam width and number of beams). The utilization of the nextgeneration of sonars, with narrow-beam angles (minimum 2.2 8 )and reduced side-lobes, such as the newly developed ME70, isexpected to provide more accurate measurements, especially forsmall or low-density schools (Trenkel et al ., 2008).

AcknowledgementsWe thank our partners in the project FADIO, Patrice Brehmer,Erwan Josse, Gala Moreno, and John Dalen, for their helpful sug-gestions and comments on software requirements. The develop-ment was supported nancially by the EU FADIO programme.Reviewers Nils Olav Handegard and Mathieu Doray are thankedfor their extensive and valued comments on the manuscript.

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