so

Sou

Echosounder

Statistical clustering

d fo

eth

junc

es

into

lus

al r

and eliminating data distortions. In effect the echoes are treated as geometrical entities, which are

classied by their shapes and positions. Direct clustering places the analysis focus on the actual echoes,

not on proxy parameters or mathematical techniques. This allows simple and direct evaluations of

results, without the need to work in abstract mathematical spaces of unknown relation to echo

he prove sonaave thr seagtaken to attr

(e.g. Hamilton, 2005; Penrose et al., 2005). However, it is still an

new

through statistical parameters (e.g. moments) of seabed echoes

Contents lists available at SciVerse ScienceDirect

w.e

Continental Sh

Continental Shelf Research 31 (2011) 20002011(redundant) echo features (1 6 6), followed by reduction to threeE-mail address: les.hamilton@dsto.defence.gov.au(Mayer, 2000). Based on this work Quester Tangent Corporation(QTC) of Canada subsequently developed the hardware and soft-ware system QTC VIEWTM. Following compensation for depth/angle sampling artefacts, the system calculates a large number of

0278-4343/$ - see front matter Crown Copyright & 2011 Published by Elsevier Ltd. All rights reserved.

doi:10.1016/j.csr.2011.10.004

n Tel.: 61 2 9381 0131; fax: 61 2 9381 0030.impedance). Sub-bottom prolers operating at lower frequencies,e.g. 8 kHz, are used to obtain information on sediment layering.A large body of literature exists on acoustic seabed classication

In the 1990s the Ocean Mapping Group of the University of NewBrunswick investigated seabed classication from echosounders(50 kHz upwards) and energies that have relatively low penetra-tion into the seabed, e.g. 0.1 m at 200 kHz (Preston, 2006), andtheir returns largely carry information on surcial seabed proper-ties related to roughness and hardness (more correctly acoustic

Hamilton et al. (1999). The present paper demonstrates amethod for rst echo classication.

1.1. The rst echo approach to acoustic seabed classicationtions to them. These descriptions may be categorical, e.g. sparseseagrass and sand, or quantitative, e.g. median grain size. Activesonar types include side scan sonar, multibeam echosounders,and conventional echosounders (depth sounders or fathometers).These three sonar types typically employ acoustic frequencies

cation. They can also be used in tandem with swathe instrumen-tation. There are two principal types of echosounder based seabedclassication methods: one using multiple echo energy character-istics, and the other using a rst echo shape approach. A descrip-tion and comparison of these two types of systems is given in1. Introduction

Acoustic seabed classication is ttype by its acoustic response to actiacoustic signature are expected to hclass, e.g. mud, sand, gravel, rock, odiver or video observations may bemidpoints of the different classes tproperties. The direct clustering method for seabed echoes is demonstrated with echosounder data

obtained in Balls Head Bay, Sydney Harbour, Australia, an area with mud, sand, and shell beds.

Crown Copyright & 2011 Published by Elsevier Ltd. All rights reserved.

cess of inferring seabedr. Areas with the samee same seabed type orrass. Grab samples andowards the geographicibute physical descrip-

active area of research. The present paper is concerned withpractical seabed classication from echosounders.

Echoes received from echosounders are typically treated asthough coming from a single point on the seabed beneath thesounder transducer, although a circular or elliptical area is usuallyensonied. Although not having the wide coverage of swathesonars, the lower cost of echosounders and their simple naturecontinues to make them extremely attractive for seabed classi-Research papers

Acoustic seabed segmentation for echoclustering of seabed echoes

L.J. Hamilton n

Defence Science and Technology Organisation (DSTO), 13 Garden Street, Eveleigh, New

a r t i c l e i n f o

Article history:

Received 2 February 2010

Received in revised form

18 April 2011

Accepted 10 October 2011Available online 20 October 2011

Keywords:

Acoustic seabed classication

a b s t r a c t

A new method is presente

by echosounders. The m

extraction, usually in con

nents Analysis. The featur

clustering or other means

technique is to directly c

extraction, and dimension

journal homepage: wwunders through direct statistical

th Wales 2015, Australia

r inferring seabed type from the properties of seabed echoes stimulated

odology currently used classies echoes indirectly through feature

tion with dimensional reduction techniques such as Principal Compo-

or principal components derived from them are classied by statistical

groups with similar sets of mathematical properties. However, a simpler

ter the echoes themselves. A priori modelling or curve tting, feature

eduction are not required, simplifying the processing and analysis chain,

lsevier.com/locate/csr

elf Research

proxies is unnecessary. This follows an approach pursued byHamilton (2007) for classication of cumulative grain size curves.The approach has subsequently been applied to oceanic wind-wave energy spectra (Hamilton, 2009) and multibeam backscattercurves (Hamilton and Parnum, 2011).

The simple concept of using clustering to classify single-valuedcurves directly does not seem to exist in the literature, possiblybecause some types of curves are not amenable to such treat-ment, and because some classication methods, e.g. factor analy-sis, may suffer from self-correlation effects in adjacent datavalues. The direct clustering method for echosounder seabedclassication allows more informed data processing, since itplaces emphasis on actual echo characteristics rather than onmathematical techniques and proxies with unknown relation toecho or seabed properties.

2. Data and preprocessing

2.1. Balls Head Bay study area

Fig. 1 charts the study area of Balls Head Bay in SydneyHarbour. The bay forms a good natural laboratory, as it has awide range of seabed types in a small area, ranging from soft,

L.J. Hamilton / Continental Shelf Research 31 (2011) 20002011 2001principal components (known as Q-factors), with clustering orsimulated annealing of the three principal components used toobtain segmentation of the data (Preston et al., 2004). A largenumber of parameters are used because the key parameters mayvary in unknown ways from area to area (Preston et al., 2004).Principal Components Analysis (PCA) is relied on to produce thecombinations of features, which best describe a particular datasetin some statistical sense. 166 features are derived from cumulativeamplitude and ratios of samples of cumulative amplitude, ampli-tude quantiles, amplitude histogram, power spectrum, and waveletpacket transform. However, the actual parameters are not dis-closed. The technique is typically very successful in classication,although problems are encountered over areas of variable bathy-metry (Hamilton et al., 1999; Biffard et al., 2005). QTC presentlyclaim that use of three principal components typically allows for90% of the observed variance (Preston et al., 2004). A previousclaim was 95% (Quester Tangent, 2002), but it is not known ifeither of these gures is adequate for seabed classication. Notethat restriction to the rst three principal components is anarticial limitation on the processing, implemented to provide apseudo 3-D visualisation of the components.

Others have adopted the general rst echo QTC approach.Durand et al. (2002) used feature extraction, PCA, and K-meansclustering to produce two and four seabed classes. Van Walreeet al. (2005) characterised echoes by six features (echo energy,second central moment, skewness, two measures of fractaldimension, and spectral skewness), followed by PCA to producethree principal components, and clustering to produce fourclasses. Tegowski (2005) clustered on three features (volumebackscatter coefcient, spectral width, and fractal dimension) toproduce four classes.

1.2. Physically based inverse modelling

Practical approaches to acoustic seabed classication arepresently phenomenological by necessity. Particular efforts tomodel echo envelopes through physical principles and inversiontechniques have been made by Clarke et al. (1988), Lurton andPouliquen (1992), and Sternlicht and de Moustier (2003).Sternlicht and de Moustier (2003) describe several other works.The most comprehensive effort is the BORIS model (Canepa et al.,1997), which continues to be developed. However, models pre-sently cannot reproduce the complexities of the real world. Toquote from Canepa et al. (1997), The physical mechanismsoccurring during the interaction of acoustic waves and the seaoor

surface and volume are still not completely understood and quanti-

ed. Quite apart from this the real seabed is not everywhere wellenough behaved to accommodate the simplications and ideali-sations of modelling. Models consequently experience ambiguity(Sternlicht and de Moustier, 2003; Preston, 2009). They are usefulin exploring and explaining general concepts, but not presentlyfor practical applications.

1.3. A different approach

In the present paper a fundamental departure is made fromprevious approaches to single beam seabed classication. Statis-tical characterisation of echoes through reduction to a set ofproxy features is not used nor is inverse modelling. The echoesthemselves are clustered directly, without the need for impositionof particular mathematical or statistical models, curve tting,feature extraction, Principal Components Analysis, linear discri-minant analysis, factor analysis, multiregression techniques, orany other approximation, distortion, or abstraction of the data.The echoes are essentially treated as geometric entities (mathe-

matically they are single-valued curves), and calculation ofFig. 1. The Balls Head Bay study area in Sydney Harbour. Depths are shown inwatery muds, muddy sands, gravelly sands, shell beds, to rock.Variable bathymetry and topography are also available, with atareas, rock walls, rocky-gravelly headlands, and scour holes. Thenorthern part of the bay is less than 10 m in depth, with shallowmuddy sands in the northwest, and soft mud in the east. Thesouthern part of the bay is 1315 m deep, with a number oftidally maintained scour holes, including a 33 m hole off BallsHead. The southern area is mostly sandy mud, with some cleanne sand patches. Shells from dead mussels and thick-walledmud oysters are found widely distributed over the southern area.The shells form strong acoustic reectors and scatterers.

2.2. Echosounder data

Single beam echo data were obtained in Balls Head Bay over1921 October 2004 with a Furuno FE-4300 200 kHz shndermetres.

L.J. Hamilton / Continental Shelf Research 31 (2011) 200020112002echosounder and QTC VIEWTM 4 hardware system. Beamwidth(3 db points) is 101 and pulse duration is 0.2 ms (Furuno ElectricCo. Ltd.). System gain and depth settings were unchanged duringthe surveys. Constant vessel speed was used whenever possible tominimise possible effects of ow noise and engine noise (seeHamilton et al., 1999).

2.3. Echo formation

The details of echo formation are well understood in generalterms, having been published many times in different forms andat different levels of complexity (e.g. Lurton and Pouliquen, 1992).A summary is given as follows. Because of wavefront curvature aping from an echosounder with a wide angle beam ensonies rsta circle on the seabed, then progressively ensonies annuli ofincreasing radii and lower grazing angles. If an amplitude envel-ope detector is used, then the signal recorded over a samplinginterval is the total specular and backscatter return from someparticular annulus. The rst part of the resulting echo shape is apeak dominantly from specular return, and the second part is adecaying tail principally from incoherent backscatter contribu-tions. Echo energies and shapes depend on seabed acousticimpedance (acoustic hardness) and physical roughness. Asmooth at bottom returns the incident ping with its shapelargely unchanged, but greater penetration into softer sedimentsattenuates the signal strength more than acoustically hardersediments. Rougher sediment surfaces provide more backscat-tered energy from the outer parts of the beam than smoothersurfaces (which simply reect the energy away from the directionof the transducer). Consequently a rougher surface is expected tohave a lower echo peak and a longer tail than a smoother surfaceof the same composition. The length and energy of the echo tailprovide a direct measure of seabed roughness. Acoustic penetra-tion into the seabed and presence of subsurface reectors canaffect echo shape through volume reverberation.

The echo shape also depends on echosounder characteristicssuch as frequency, ping duration, ping shape, output power, beamwidth, and beam pattern. Spherical spreading and absorptioncorrections are necessary, depending on sounder frequency andsurvey depth. Surveys should be executed at constant vesselspeed to avoid changing background noise values, and echosoun-der gain and system settings should not be changed during asurvey (Hamilton et al., 1999). On the average, these factors thenaffect all echoes in the same way, and their actual effects need notbe known. Systems then need not be calibrated in order to beused, and beam patterns need not be measured, one of theadvantages of echosounder based classication. Results fromdifferent systems are not directly compatible, although corre-spondences can be found in some cases (Hamilton et al., 1999).

Return echo shapes can vary markedly over a small timeinterval, even for the same bottom type. As a result of ship andsensor movements and natural variability the returns from anyparticular angle are of a random nature, sometimes adding andsometimes subtracting as bottom facets lying at slightly differentangles and depths are encountered. In particular, returns fromharder surfaces such as rock tend to have greater roughness andmore random orientation of seabed facets than other sediments,resulting in widely varying return shapes and energies. Echoes arealso subject to noise, natural environmental variability, andechosounder instability. To obtain acoustic signal stability setsof pings are usually averaged. Over rougher terrain simpleaveraging may not help ping stability, and can act to reduceoverall ping levels from their true value, causing rocky surfacesto be classed as muds (Hamilton et al., 1999; Hamilton, 2001). Inthis circumstance a different echo compositing method can be

used, e.g. Hamilton et al. (1999) suggested using the average ofthe one-third highest values in a ping set, under the assumptionthat higher energy returns are least affected by roughness andvessel motion effects.

Calibration of a particular acoustic seabed classication sys-tem is made by visiting areas with known seabed type, and notingor recording the system response at those sites. System calibra-tion and classication then become a function of the bottomsampling strategy. Alternatively, seabed sampling may be tar-geted to areas with spatially homogeneous acoustic seabedresponses. The direct clustering methods of the present paperare suited to the second strategy. Classication can depend on thepurpose of the user e.g. a mapping of sh habitat could produce adifferent classication from a mapping allied to grainsize. It is thegeneral experience that useful classications can be obtainedwith echosounders if due care is taken, although calibration is notalways easy or unambiguous (Hamilton, 2001), as particular echoshapes need not have a unique cause. Shell components inparticular can cause unpredictable returns. Because of ambiguity,a calibration for one area can not be used for other geographicalareas. This generally precludes the use of a library of seabedresponses to indicate seabed type, except perhaps in very generalterms. Further comments are given in Hamilton (2001).

2.4. Preprocessing

Two operations are necessary before echoes can be classied.The rst allows for dilation or contraction of echoes with depth,the result of using a xed sampling interval in time, rather thansampling at a set of evenly spaced angles (Caughey and Kirlin,1996). The second operation averages or stacks sets of echoes toremove variability. It has been found better to calculate echoparameters from echo stacks, rather than to calculate parametersfrom individual echoes and then average (e.g. Preston et al., 2004),also the experience of the present author.

2.5. Effects of sampling artefacts and depth on echo shape and

duration

Echo duration is dependent on depth, through wavefrontcurvature and echosounder beamwidth. Contributions to echoduration also arise from echosounder pulse duration, sedimentmacro-roughness, and volume reverberation from penetrationinto the sediment (e.g. Preston, 2003, 2006). The duration dueto beamwidth alone is (2d/c)(1/cos(D/2)1), where d is the depthmeasured vertically, c is the sound speed, and D is the echosoun-der beam width (nominally specied between the 3 db points).If a constant sampling rate fs is used, more samples will be takenbetween any two particular angles as depth increases, causingsignal dilation, even for seabeds with similar sediment properties.In order for two returns at different depths d and d0 to maintainthe same time/angle relationship the data are resampled asfresamp(d0/d)fs, where d0 is a reference depth near the meansurvey depth, and fs is the original sampling rate (Caughey andKirlin, 1996; Preston, 2006). This correction treats echo durationas arising entirely from beamwidth geometry. Output echosoun-der pulse duration and extensions to echo duration from rough-ness and volume reverberation must be small in comparison.

When the contribution to echo duration at a particular depthdue to roughness, penetration, and output pulse duration issignicant compared to echo duration calculated from beam-width geometry, echo durations are no longer proportional todepth. The Caughey and Kirlin (1996) correction is then notadequate for the depth/sampling compensation. This occurs inshallow water, which could be dened through some fraction ofthe echo duration caused by beamwidth alone, e.g less than

0.8 may be considered reasonable. Preston (2006) outlines a

Each curve in the entire data set is assigned to one (and only one)

L.J. Hamilton / Continental Shelf Research 31 (2011) 20002011 2003method to make approximate corrections to echo durations in theshallow water regime. This method adopts a nominal penetrationdepth, and a nominal seabed roughness, in order to calculatecorrection factors for the time/angle sampling artefacts.

For the Furuno FE-4300 200 kHz echosounder, with nominal3 db beamwidth (D) of 101, and pulse duration of 0.2 ms, echoduration due to beamwidth alone is less than output pulseduration for depths less than 39.3 m. Contributions to echoduration from roughness and sediment penetration can onlyfurther reduce the relative beamwidth contribution. Effectivebeamwidth may be wider than 101, as beams do not suddenlycut off at the 3 db points. For effective beamwidth 151 (taken asan extreme case for the nominal 101) and nominal seabedroughness scale and penetration depth both set to 0.1 m (takenfrom Preston (2006) for 200 kHz), echo duration due to beam-width is less than 0.8 of total echo duration shallower than 163 m.Since maximum survey depth is 33 m in a scour hole (Fig. 1), withaverage depth of 1112 m, the calculations indicate that theshallow water form of corrections (Preston, 2003, 2006) isrequired for the Balls Head Bay data.

Distortion may be minimised by compensating echoes to adepth midway between minimum and maximum survey depths,which for Balls Head Bay is close to 15 m. For the nominalroughness scale and penetration depth both set to 0.1 m, thePreston (2006) calculations indicate that echo duration for 5 mdepth should be dilated to 110% compared to echoes from 15 mdepth. Echo durations from 25 m should be compressed to 91% ofthe measured duration. For 10 and 20 m depths the adjustmentreferenced to 15 m is 5% rather than 10%. These are relativelysmall adjustments compared to the variability typically seen inecho durations from any one depth (as the data will show). This isespecially so, given the compromise nature of the shallow watercorrections. For these reasons it was decided not to apply anydepth compensation corrections to echo durations. This might notbe viable for a wider depth range or different echosoundercharacteristics.

2.6. Echo selection, averaging, and energy normalisation

Echoes are detected and selected for averaging or compositingby methods described in Hamilton (2001; Section 5.1). Anindividual ping record begins with the high energy echosounderoutput ping, followed by transducer ringing, then the rst echo,and multiple echoes. The ping record ends at the start of the nextoutput ping. Calculations avoid the ringing, the duration of whichmust be determined for each transducer. The background noiselevel is calculated, then processing steps backwards from the endof a ping record (this helps to avoid any spikes and multiplereturns occurring after the rst echo), and the most energeticpeak above the noise in the individual ping record is found. Thepeak is calculated as a running average of three or more values.The echo is accepted only if the peak exceeds some threshold ofthe maximum possible system amplitude range less the noise,e.g. 5%, and also if it exceeds the duration of the output ping.These two criteria act to eliminate spikes and poorly formedlower energy echoes.

Processing then steps backwards from the peak to nd thestart of the echo, then forwards from the peak to nd the end ofthe echo. Starts and ends may be found by tests of the form x of yconsecutive values must be above/below a threshold. Ten pingrecords are processed. Echoes are aligned to have a common starttime, and are individually corrected for spherical spreading if thishas not already taken place in hardware. Absorption was ignoredfor the relatively shallow survey depths. Echoes are normalised tohave uncalibrated energy of one unit. Preston (2006) normalises

peak amplitude to one unit. Both normalizations remove relativeof the groups. The individual curve most closely approximatingthe central tendency of a cluster is termed a medoid (Kaufmanand Rousseeuw, 1990).

The CLARA algorithm uses a k-medoids clustering approach. Themeasure of the effectiveness of a clustering determined by krepresentative objects is dened as the sum of distances (e.g.Manhattan) between each object and the most similar representativeobject or medoid. Representative objects are selected by an iterativeapproach. The rst representative object is the one for which the sumof distances to all other objects is the minimum. This object is themost centrally located in the set of objects. This object is retained,and the next representative object is similarly selected to minimisethe sum of distances. When k objects have been selected the initialset of representative objects is improved by swapping each repre-amplitude information between echoes, although this can berecovered. If three or more good echoes are found in the pingset, then the good echoes are averaged or composited, otherwisethe ping set is discarded. A check is made that consecutiveaverage echo depths do not differ by a depth dependent thresh-old. This avoids some errors and prevents averaging of echoes onsteeper slopes, or when passing from at areas to channels orholes. These techniques allow fully automatic (and real-time)selection of echoes. User intervention is not required, so long as itis accepted that occasional errors may occur. For means ofcompositing echoes other than simple averaging, see Hamilton(2001). The 32,536 raw echoes were processed to provide 3149averaged echoes.

3. Methods

3.1. The CLARA statistical clustering algorithm

The statistical clustering algorithm employed is the CLARA(Clustering Large Applications) algorithm of Kaufman andRousseeuw (1990). The present analysis uses FORTRAN code forCLARA downloaded from the Internet at http://lib.stat.cmu.edu/general/clusnd. CLARA is also available in other languages and inseveral statistics packages. CLARA is a general clustering algo-rithm, and was not designed to cluster curves. It was shown to besuitable for this purpose by Hamilton (2007, 2009), subject tosome limitations, and subject to choice of distance metric. Thisapproach is fundamentally different from the conventional con-cept of clustering, which treats data objects as vectors of co-ordinates in a multi-dimensional space, not as curves. In simpleterms curves are here considered to be sets of connected pointswith the same units, for which the order of points is important(abscissa values increase monotonically), and which may describea physical phenomenon. Curves are required to be single-valued.Examples are wind-wave energy spectra. The difference in theconventional concept of statistical clustering and the concept ofstatistical clustering of single-valued curves is more than amathematical nicety. For clustering of curves with the Manhattanmetric, a simple 2-D geometric explanation or validation of theaction of the clustering arises. For two curves, the Manhattandistance metric measures the difference in area bounded by thecurves with the abscissa (Hamilton, 2007). Other clusteringmetrics, e.g. entropy, do not have a geometrical interpretation.

CLARA is used to place echoes into different groups or clusters.Each group contains echoes of similar shape, and each group has adifferent basic echo shape than other groups. A distance metric,e.g. Euclidian or Manhattan, is used to decide whether echoes aresimilar or dissimilar in properties. An echo is assigned to a groupif this optimises a global cost function (see the next paragraph).sentative object with all other non-representative objects, and by

der based seabed classication, including those which attempt tomodel the echo envelope. In actuality the meaning of the map-

L.J. Hamilton / Continental Shelf Research 31 (2011) 200020112004calculating the value of the clustering for each case. The representa-tive object is replaced if the overall sum of distances is minimised.This process is continued iteratively until no more improvement isobtained. Because all possible replacements are considered in thisiteration process, results do not depend on the order that objects areinput to the CLARA algorithm, unlike K-means algorithms.

3.2. Methodology for the CLARA algorithm to cluster curves

A methodology for optimum use of CLARA for clustering ofcurves is developed in Hamilton (2007, 2009). A combination ofnon-standardisation of parameters and Manhattan distancemetric was found to produce best results. An alternative metricis Euclidian, but this can over accentuate differences betweencurves. However, standardisation allows identication of outliercurves and sets of curves most different from others. This facilitycan be very useful in identifying more unusual seabed types,particularly those occupying only small spatial areas, and inrevealing data errors. Another useful characteristic of CLARAclusters is that they appear independent of data numbers.Clusters holding very small numbers of curves can arise inclassication of large data sets if curves have properties geome-trically different from other curves (Hamilton, 2007, 2009).

Acoustic seabed classication methods are typically only ableto form four or ve useful classes. Estimation of the number ofclasses present in a data set may be made by quasi-independentstatistical estimators used in conjunction with the clustering.Kaufman and Rousseeuw (1990) supply a Silhouette Coefcientfor this purpose. The user clusters from two groups upwards untila maximum value in the coefcient is passed, with the maximumpotentially indicating the optimal number of clusters. However,Hamilton (2007) did not nd this coefcient useful for data setsfor which clearly separated clusters did not exist. A more effectivemethod of assessing cluster numbers is to cluster from twogroups upwards until no more useful information is obtained inclustering space (revealed by examining overplots of medoids,and overplots of all echoes in a cluster), or in geographical space.Requesting more clusters than indicated by some particularstatistical indicator is not in violation of statistics or statisticalmethods. In effect it simply means that the particular informalstatistic to estimate number of clusters in a data set has beenrelaxed or modied (Hamilton, 2006).

A feature of CLARA useful for classication of curves is that itcan successfully partition a set of similarly shaped curves forminga quasi-continuum in their space (Hamilton, 2007, 2009). Somealgorithms cannot do this in a sensible manner, and CLARA itselfwas not intended to be used for this purpose. Echoes may form aquasi-continuum when seabed properties change gradually fromone place to another, rather than jumping from one type to acompletely different type. In this circumstance as many or as fewclusters may be requested of CLARA as the user nds useful tofollow the evolution or spatial distributions of the seabed proper-ties. It follows that there is usually no such thing as an absolutenumber of clusters for a geodata set. The optimal number ofclusters in some statistical sense may not be optimal in thegeographic domain. A good example of this is given in Hamilton(2007). The Silhouette coefcient indicated that three clustersexisted in a set of cumulative grain size curves. The three clusterscorresponded to major visible groupings or divisions in the data,but a manual analysis had formed 20 clusters. The CLARAalgorithm was requested to form 20 clusters, and a very goodclustering resulted which was superior to the manual clustering.

The effectiveness of the clustering can be checked by thefollowing analysis chain. (1) examine overplots of clustermedoids, (2) examine overplots of the curves forming each cluster

for uniformity of properties (shape, location, and centralpings depends on the particular equipment set used, and ambi-guity may occur, so that groundtruthing is a necessary part of thevalidation.

Many clustering algorithms require too much processingpower, computer memory, or processing time to be tenable foranalysis of large data sets. Software programme CLARA over-comes these limitations by coupling statistical sampling andclustering techniques. The CLARA algorithm is intended to clustera minimum of 100 objects. The algorithm rst clusters severalsets of randomly chosen subsamples, then uses the particularsubsampling returning best results (evaluated as previouslydescribed in the section The CLARA Statistical Clustering Algo-rithm) to cluster the entire data set. This provides a fastalgorithm suitable for processing of large data sets, at the possibleexpense of accuracy.

Sensitivity tests of the CLARA algorithm by the present authorhave shown the sampling scheme is robust, and that inaccuracyarising from the CLARA sampling methods is not an issue pastsome critical (and comparatively low) choice of number of objectsin the subsamplings. The robustness of CLARA to relatively smallsubsample population size provides many advantages. Fast quick-look explorations of large data sets (e.g. 50,000 objects) can bemade in a few minutes. This enables determination of the optimalnumber of clusters to be made relatively quickly, and is alsouseful for estimations of data quality.

4. Analysis

4.1. Groundtruth

Groundtruth is available from surcial samples (some arevisual descriptions only) (19802003), towed underwater video(July 1999), 455 kHz Klein 5500 sidescan sonar imagery (1999),and a 50 kHz RoxAnn acoustic seabed classication system(May June 1999). Dredging is not carried out in Sydney Harbour,so that the ve year time span of major data acquisition is notexpected to present any difculty.

Sidescan sonar backscatter imagery and RoxAnn echo parametersE1 and E2 all broadly separate the bay into two areas. E1 is anacoustic backscatter index related to surface roughness, and E2 is ameasure of acoustic impedance, often referred to as acoustic hardness(Hamilton et al., 1999). Low backscatter is found east of a line runningfrom 300m west of Manns Point to Balls Head, with higher back-scatter west of this line (Fig. 2). The RoxAnn data map the full extenttendency), (3) examine the resulting geographical distributionsof classes. GIS techniques aid the latter examinations. It is usuallybetter to request many clusters, rather than a few, for large datasets. If too many clusters have been requested, indicated, e.g., iffragmentation is observed in geospace, then similar clusters canbe amalgamated, or a lower number of clusters can be requested.Not requesting enough clusters can force curves with differentproperties into the same cluster, and can suppress outlier detec-tion. Data exploration is an essential part of any examination oflarge data sets. (4) The nal part of the analysis chain is ground-truthing. Note however, that because the mappings are directlyrelated to echo shapes, not to echo features, in principle it isalready known that the mappings have a physical meaning.Ground-truthing then becomes a separate activity from theanalysis. In concept the groundtruth is then only used to attachdescriptors or labels to the acoustic mappings, and the ground-truth does not have to be invoked to prove the analysis. This setsthe present method apart from previous techniques of echosoun-of the high backscatter area, which video, grabs, and dredges show is

L.J. Hamilton / Continental Shelf Research 31 (2011) 20002011 2005caused by dead mussel and mud oyster shell. The change frompresence to absence of shell in the video transect in the centre of thebay coincides closely with the change from high to low backscatter inthe sidescan and RoxAnn mappings. The 50 kHz RoxAnn systemfunctions as a very good shellbed nder, particularly through higherE2 values. The mud oyster shell is thick walled (13 cm), and up to1015 cm in length, and 57 cm in width. In some areas the shellcompletely covers the surface.

Away from headlands some extremely broad mud wt% contoursand visual sediment descriptions show much of the area east of theManns Point/Balls Head line to be soft mud, with mud% weight7098%. West of this line an eastwest trending sandy area (with

Fig. 2. Groundtruth for Balls Head Bay. (a) Klein 5500 sidescan sonar imagery, underwFilled symbols indicate the presence of shell, and unlled symbols and plus symbols (denote sand without shell; (b) distribution of RoxAnn E1 values. Larger symbols denote

values; and (d) broad contours of mud by wt% of sample (Udden-Wentworth grainsiz

Grey-lled triangles denote mud weight 020%, grey-lled squares 2030%, circles (K

squares () 70100%.zero to 30%mud) is centred on the unlled cross symbol in the centreof Fig. 2a. Another sandy area lies immediately west of the troughprojecting north from the deep scour hole off Balls Head. These twosandy areas are separated by muddy sediments. The groundtruthallows a basic sediment pattern to be constructed, but details aresketchy, with the sediment samples indicating patchiness fromManns Point to Balls Head in particular.

The east-west division into soft mudsandy sedimentsappears to be a combination of geological and shipping factors.Oil tankers transit from west of Balls Head to berths in thechannel north of Manns Point. Distribution of higher RoxAnn E2values follows the shipping channel closely, in the study area and

ater towed video transects (squares, &), and sampling locations (circles, JK).

) the absence of shell. Larger unlled plus symbols in the centre of the gure ( )

higher values; (c) distribution of RoxAnn E2 values. Larger symbols denote higher

e scale). Unlled triangles (D) are sample positions with visual descriptions only.) 3040%, pentagons 4050%, black lled triangles (m) 5060%, and black lled

farther east, indicating that propeller action may scour away thesofter sediments to expose relict shell and sands. Sediments arevisibly entrained into the water column by ferries turning thesevessels off Manns Point, possibly causing the scour hole in thatlocation, and the rougher-harder area in the RoxAnn data.

4.2. The number of clusters

CLARA was requested to form 2, 4, 8, 12, 16, 24, 30 clusters. Eachclustering takes a minute or so with the present data set, so thatprocessing time is not a factor. The rationale in proceeding in thisfashion is that the number of useful clusters is unknown beforeanalysis. Also, the optimal number of clusters in some statistical sensemay not be optimal in the geographic domain. This is particularly so

for quasi-continuums (or data clouds in feature spaces), but is notlimited to that situation. It is a simple matter to visually scan themappings formed with different numbers of clusters for spatialcoherency, persistence of particular spatial congurations and so onto decide (a) whether or not a viable analysis is possible, (b) thenumber of useful clusters for a particular purpose, and (c) whetherforming more clusters reveals particular spatial congurations ofseabed properties not visible to smaller numbers of clusters. Resultsfor the two, four and eight cluster cases are shown.

4.3. Cluster mappings and relation to groundtruth

Mappings of cluster results are shown in Fig. 3. Medoids(central tendency of the clusters) are graphed in Fig. 4, and echo

es.

es, c

L.J. Hamilton / Continental Shelf Research 31 (2011) 200020112006Fig. 3. Mappings for 2, 4, and 8 classes by direct CLARA clustering of seabed echoyellowclass 2, blueclass 3, redclass 4; (c) eight-clustering. Class1open circlclass 7mauve, and class 8open triangles. (For interpretation of the references to co(a) Two-clustering. Greyclass1, redclass 2; (b) four-clustering. Greyclass 1,

lass 2red, class 3yellow, class 4green, class 5lled triangles, class 6blue,lour in this gure legend, the reader is referred to the web version of this article.)

Fig. 4. Medoids (central tendency measures of

Fig. 5. Overplots of echoes for two- and four-clusterings produced by directCLARA clustering of seabed echoes. Notation: 4-3 is cluster 3 of the 4-clustering.

L.J. Hamilton / Continental Shelf Research 31 (2011) 20002011 2007echo clusters) for the mappings of Fig. 3.overplots in Figs. 5 and 6. The two-clustering provides a spatiallycoherent mapping which separates the deep scour hole off BallsHead from other locations (Fig. 3a). Echoes in the scour hole classhave much longer backscatter tails than the other class, indicativeof slopes, very rough sediments, or hard ground such as rock.Exposed rock forms the steeper northern side of the hole, andlarge thick-walled shells were found in the hole. A few isolatedareas elsewhere in the bay with the same classication as the holeare associated principally with slopes. These areas appear in all ofthe clusterings. Because of the extremely high spatial correlationbetween the acoustic classes and geomorphic features in thesurvey area, the physical meaning of the acoustic classication isimmediately obvious.

The four-clustering also provides a spatially coherent mappingin which the northern bay area has different acoustic properties(class 2) from the southern bay (class 1) (Fig. 3b). This spatialdivision closely matches the groundtruth of Fig. 2, which similarlydivides the bay into northern and southern areas of differentseabed properties. Again there is no doubt as to the meaning ofthe acoustic classication, and it clearly provides a higher spatialresolution of seabed types than the two-clustering. High spatialcorrelation of the northmost class 2 with groundtruth identies itas soft mud with mud wt% over 70%. The southern bay class 1 isgenerally associated with mud wt% less than 70%. Class 2 alsooccurs in the south as the eastern outskirts of high mud con-centration in Snails Bay. The Balls Head scour hole and slopesagain appear as having much higher roughness properties thantheir surrounds (classes 3 and 4). The mapping appears useful as abroader acoustic segmentation of seabed acoustic properties, aview supported by the conguration of cluster medoids (Fig. 4)and overplots of all echoes in each class (Fig. 5). Overplots of allechoes in each class are regular in geometrical properties (shape,

L.J. Hamilton / Continental Shelf Research 31 (2011) 200020112008central tendency, position of the echo peak). A deal of variationoccurs within each class, but medoids differ from each other inshapes and locations. Cluster 1 and 2 medoids are similar, buttheir geographical distributions are spatially separate. Cluster1 has a relatively longer backscatter tail than cluster 2. Thiswould usually indicate cluster 1 is rougher than cluster 2, whichis veried by its spatial correlation with the distributions of shelland higher RoxAnn E1 roughness values. In Figs. 4 and 5 the echopeak appears progressively later in the echo as backscatter tailslengthen. When not over slopes the time from echo start to echopeak is physically related to sediment roughness (e.g. Hamilton,2001; Section 4.3), as is the duration of the echo. Properties ofindividual echoes within any one of the four classes of Fig. 5 varyabout the central tendency of their cluster, with a range of timesto echo peak, and in what might be described as jaggedness in theechoes. Clustering into a relatively few classes effectively acts as ifthe echoes in each cluster are rst smoothed or ltered, eventhough smoothing is not actually performed.

The eight-clustering is mapped in Fig. 3c, with medoids inFig. 4 and echo overplots in Fig. 6. In places the mapping appearsfragmented compared to the four-clustering (Fig. 3b), and classes2 and 4 have much the same distribution. This indicates the limits

Fig. 6. Overplots of echoes for the eight-clustering.of the clustering with respect to providing a useful spatialmapping may have been reached or exceeded, i.e. it appears thattoo many clusters have been formed. This is possibly indicated bythe closeness of some of the medoids in properties, although thisin itself is an insufcient indicator. Spatial distributions andgroundtruth must be examined. The eight-clustering is used todemonstrate the benets of data exploration. Fig. 7 chartsindividual clusters in order of echo duration for their respectivemedoids, i.e. in general order of expected increasing roughness.Apart from class 6 the eight clusters generally have coherentspatial distributions, and all classes have rather clear relations toseabed properties. Clusters 3 and 6 dominantly occupy the east-ern and western halves respectively of the shallow northernembayment. Class 3 is associated with mud weight over 70%,and class 6 is associated with mud weight less than 70%. Thisprovides an enhanced resolution of seabed properties comparedto the four-clustering. Classes 6 and 2 overlap in spatial distribu-tion in the south and east of the bay, but elsewhere they arespatially separate, and supply more information than the 4-clus-tering. In the south and east they must be joined as a mixed class,a factor attributed to natural variability. Since the classes sharethe same depth range in this area, the spatial overlap cannot be anerror of depth compensation. Classes 2 and 4 both have mudweight less than 70%. Class 4 lies interior to class 2, and isinterspersed with class 2. The rst impression is that classes 2 and4 should be amalgamated, reproducing class 1 of the four-clustering. However, their medoids (Fig. 4) appear sufcientlyseparated for them to represent different classes, as turns out tobe the case. The groundtruth indicate that class 4 coincides withshell concentrations lying on the surface of class 2, which make itrougher than class 2. Classes 1 and 5 are associated with slopesthroughout the bay, and with the Balls Head scour hole, eitherthrough slope or increased surface roughness. Classes 7 and8 occur almost wholly in the Balls Head scour hole. Geographi-cally class 8 lies within class 7, with a spatial distribution separatefrom class 7. Class 8 occurs particularly on the north side of thehole, where it may be associated with both steeper slopes and anexposed rock surface. Class 8 has the same distribution as higherRoxAnn E1 values (Fig. 2b). E1 is a direct energy measure,whereas class 8 is based on the shape of a unit energy echo.These two different indications of seabed conditions effectivelyverify each other by their spatial coincidence. The new informa-tion from the eight-clustering provides an enhanced mappingcompared to the four-clustering, even though eight clustersappears to have approached the useful limits of the clusteringfor purposes of geographical mapping.

The energy normalised echo medoids and echo overplots graderelatively smoothly in shape properties as a function of the timefrom echo start to echo peak (Figs. 4 and 5). This condition isenhanced by the energy normalisation, which removes absoluteamplitude information, but is a real feature of this data set.Discrete clusters completely separate in properties from all otherclusters do not exist in this data set. In these circumstances classboundaries are largely articial constructs imposed on the data,and largely depend on the number of classes requested. Accord-ingly, as many or as few clusters may be formed as foundnecessary to identify sediment patterns and evolution of propertytrends.

For the Balls Head Bay data, eight clusters is indicated to be anupper limit on the number of useful clusters, and the number ofclusters would usually be chosen at this or some lesser number.However, as part of the exploration of the clustering methodol-ogy, geographical mappings for 12, 16, 24, and 30 clusters wereexamined. Results are not shown, not being directly useful to theanalysis. Spatially coherent distributions occurred in some areas,

but other areas had a polka-dotted appearance, indicating too

L.J. Hamilton / Continental Shelf Research 31 (2011) 20002011 2009many clusters were formed for those locations. Useful spatialinformation was obtained for these areas by amalgamatingclusters having both adjacent medoids and overlapping spatialdistributions. Forming classes in this manner is expected to bevery useful for some data sets. This is because clusters with echoproperties very similar to other clusters may sometimes havegeographical locations or spatial congurations distinctly differ-ent from other clusters. This potentially useful information willnot be found if it is not sought. A mapping of nine amalgamatedclasses formed from the 16-clustering was similar to the eight-clustering. The soft mud class 3 of the eight-clustering was splitinto three geographically separate and spatially coherent distri-butions, but the groundtruth could not separate them.

Fig. 7. Spatial distribution of individual classes for the eight-clust5. Discussion

5.1. Some key echo characteristics

Clustering is a postprocessing operation. Feature extractionmay be viable for useful real-time operations, even if not asaccurate as processing the echo in its entirety. According to avisual examination of the cluster medoids of Fig. 4, the energynormalised echoes can be regarded as being characterised princi-pally by (1) the time from the echo start to the echo peak (risetime), (2) echo width (total width is usually, but not always, moresensitive to echo shape than half-width because of echo skew-ness), and (3) echo peak height. Although useful for classication,

ering produced by direct CLARA clustering of seabed echoes.

allow a visually informed and direct evaluation of results, in-

L.J. Hamilton / Continental Shelf Research 31 (2011) 200020112010Echosounder based seabed classication is not an exact pro-cess. Corrections to echo shapes and energies necessary toaccount for wavefront curvature and sampling at equal intervalsof time, rather than at equal angles, are not exact (see Preston,2006). It is difcult to impossible to account for seabed slopeeffects (Biffard et al. (2002)), a factor compounded by variablebathymetry. Ambiguity in acoustic responses from some seabedtypes may result (Hamilton et al., 1999). When well deneddifferences between physical properties of seabed classes do notexist, the spatial boundaries between acoustic classes can beentirely articial. The acoustic classes are then indicators ofgeneral property trends, and are not necessarily absolute in somesense. Seabed classication is used as a proxy for habitat classi-cation, but it follows from the previous remark that directrelations between habitat and seabed acoustic classes might notexist. In such circumstances supervised classication may bemore useful for habitat classication than the unsupervisedclassication of the present analysis. Echosounder seabed classi-cation is highly useful, but users must be aware of the limita-tions of the technique.

6. Conclusions

After preprocessing, direct statistical clustering of actual echoesfrom Balls Head Bay, Sydney Harbour was sufcient to achievesegmentation of the seabed into areas with similar acousticresponses to echosounder pings. Feature extraction and dimen-sional reduction techniques were not necessary. This was veriedby sidescan sonar, a RoxAnn system, underwater video, and seabedsamples. To be most useful, mappings obtained from direct cluster-ing of actual echoes are in need of labelling with inferred seabedcharacteristics. Note however, that in themselves they do not needverication. Because actual echoes are classied directly and intheir entirety by the direct clustering technique, there is no doubtthat geographical class mappings do represent different acousticseabed responses. In contrast feature extraction and dimensionalreduction methods of classication need both verication andlabelling, as does modelling, because they process echo character-istics indirectly, sometimes in mathematical spaces with unknownrelation to echo properties. The mappings produced from directclustering of echoes are not absolute in a physical sense becausethey depend on echosounder characteristics. However, they can stillpeak height is not directly representative of acoustic reectivity(or acoustic hardness), since the area under the amplitude-squared curve (uncalibrated energy) has been set to one unit inthe analysis. The other two parameters are physical propertiesdirectly related to seabed roughness, and are independent ofenergy, providing a survey is not made over a depth range largeenough for attenuation effects to signicantly change the signal tonoise ratio of the echoes. Classifying on these three parameters,and/or derived parameters which accentuate these properties(e.g. moments, skewness, kurtosis) may be sufcient to providea basic echo characterisation suitable for real-time classication.Successful seabed classication was obtained in postprocesssingby van Walree et al. (2005) with six features, and by Tegowski(2005) with three features. Feature selection is not furtherdiscussed in the present paper, but note that direct clustering ofechoes provides a basis to nd optimal echo features with knownphysical relation to echo characteristics. Clusters or classes givenby the features should align with those given by direct clustering.There can be no better validation.

5.2. Limitations of echosounder based seabed classicationbe used as a standalone acoustic segmentation.nitely more meaningful than visualisation of principal compo-nents in a mathematical pseudo 3-space. Many reports onacoustic seabed classication do not present so much as a singleecho, and some acoustic seabed classication systems do notstore or display echoes. This is not conducive to obtaining reliableand veriable results. Useful though they undeniably are, dimen-sional reduction techniques provide only indirect or secondhandanalyses of data. Coupling the concept of direct clustering ofsingle-valued curves (Hamilton, 2007, 2009) with statisticalsampling and clustering techniques such as those used by theCLARA algorithm of Kaufman and Rousseeuw (1990) means thatthe so-called curse of dimensionality (Bellman 1961) no longerexists for many applications. Dimensional reduction approacheshave dominated thinking on large data sets in several areas of thegeosciences for several decades, but for many data sets able to beexpressed as single-valued curves they may not be necessary.

References

Bellman, R., 1961. Adaptive Control Processes: A Guided Tour. Princeton Uni-versity Press, Princeton.

Biffard, B., Bloomer, S., Chapman, R., 2005. Single beam seabed classication:direct methods of classication and the problem of slope. In: Pace, N.G.,Blondel, P. (Eds.), Boundary Inuences in High Frequency, Shallow WaterAcoustics, University of Bath Press, Bath, pp. 227232.

Canepa, G., Bergem, O., Pouliquen, E., 1997. The implementation of BORIS-3D:BOttom Response from Inhomogeneities and Surface, Version 1.0. SACLANT-CEN Special Report, M-125. SACLANT Undersea Research Centre, La Spezia,Italy.

Caughey, D.A., Kirlin, R.L., 1996. Blind deconvolution of echosounder envelopes.Proceedings of the IEEE International Conference on Acoustics, Speech andSignal Processing ICASSP 6, 31493152.

Clarke, T.L., Proni, J.R., Seem, D.A., Tsai, J.J., 1988. Joint CGS-AOML (Charting andGeodetic Service Atlantic Oceanographic and Meteorological Laboratory)Acoustical Bottom Echo-Formation Research I: Literature Search and InitialModelling Results. NOAA Technical Memorandum ERL AOML-66.

Durand, S., Le Bel, M., Juniper, K., Legendre, P., 2002. The use of video surveys, ageographic information system and sonar backscatter data to study faunalcommunity dynamics at Juan de Fuca Ridge hydrothermal vents. CahiersBiologie Marine 43, 235240.

Furuno Electric Co. Ltd. Furuno Operators Manual Echo Sounder FE-4300. No. OM-E2315-0B. Nishinomiya, Japan, 40 pp.

Hamilton, L.J., 2001. Acoustic seabed classication systems. DSTO Technical NoteDSTO-TN-0401, 66 pp. /http://www.dsto.defence.gov.au/corporate/reports/DSTO-TN-0401.pdfS.

Hamilton, L.J., 2005. A bibliography of acoustic seabed classication. CooperativeThe success of the direct clustering method for processing ofechoes for Balls Head Bay can be interpreted as meaning that therst order information inherent in the echoes was sufcient initself for classication. Echo information did not have to beaccentuated through calculation of nonlinear characteristics suchas higher order moments, or spectral properties. This is despitethe narrow 101 beamwidth used. Multibeam sonar seabed angularbackscatter response curves suggest beamwidths over 301 may beoptimal for echosounder seabed classication in shallow water(Hamilton and Parnum, 2011). Nevertheless, features calculatedfrom higher order statistics may be helpful in discriminatingbetween some types of seabeds. For example, Tegowski (2005)used a measure of fractal dimension to capture the jaggedness orcomplexity in echoes. The direct clustering indicates simplegeometrical echo features which may be capable of differentiatingecho shapes and hence seabed types. This information may beuseful for real-time acoustic seabed segmentation.

The simple approach to seabed classication enabled throughdirect clustering of echoes places the focus of attention on theprimary data (the actual echoes), where it should always be.Emphasis on mathematical and statistical echo parameters andtechniques is replaced by a more informed and physically basedapproach to data exploration. Simple displays of classied echoesResearch Centre for Coastal Zone, Estuary, and Waterway Management

Technical Report No. 27./http://www.coastal.crc.org.au/pdf/TechnicalReports/27-Hamilton_Acoustic_Biblio.pdfS.

Hamilton, L.J., 2006. Comments On Orpin, A.R., Kostylev, V.E. (2006). Towards astatistically valid method of textural sea oor characterization of benthichabitats. Marine Geology 225(14), 209222. Marine Geology 232 (12),105110.

Hamilton, L.J., 2007. Clustering of cumulative grain size distribution curves forshallow-marine samples with software program CLARA. Australian Journal ofEarth Sciences 54, 503519.

Hamilton, L.J., 2009. Characterising spectral sea wave conditions with statisticalclustering of actual spectra. Applied Ocean Research. doi:10.1016/j.apor.2009.12.003.

Hamilton, L.J., Mulhearn, P.J., Poeckert, R., 1999. A comparison of RoxAnn and QTC-View acoustic bottom classication system performance for the Cairns area,Great Barrier Reef, Australia. Continental Shelf Research 19 (12), 15771597.

Hamilton, L.J., Parnum, I., 2011. Seabed segmentation from unsupervised statis-tical clustering of entire multibeam sonar backscatter curves. ContinentalShelf Research 31 (2), 138148.

Kaufman, L., Rousseeuw, P.J., 1990. Finding Groups in Data: An Introduction toCluster Analysis. John Wiley, New York.

Lurton, X., Pouliquen, E., 1992. Automated seabed classication system for echosounders. In: Proceedings OCEANS92. Mastering The Oceans Through Tech-nology, vol. 1, pp. 317321.

Mayer, L., 2000. Normal incidence classication methods. Lecture 19. Fourth Asia-Pacic Coastal Multibeam Sonar Training Course, Cairns, Australia, 1419August 2000.

Penrose, J.D., Siwabessy, P.J.W., Gavrilov, A., Parnum, I., Hamilton, L.J., Brooke, B.,Ryan, D.A., Kennedy, P., 2005. Acoustic Techniques For Seabed Classication.CRC Technical Report No. 32. 130 pp. /http://www.coastal.crc.org.au/Assets/03CaseStudies/03_10References/Papers/Sonar/CRCliteraturereview_v21.pdfS.

Preston, J.M., 2003. Resampling sonar echo time series primarily for seabedclassication. U.S. Patent Application Serial Number 449914.

Preston, J.M., 2006. Acoustic Classication of Seaweed and Sediment with Depth-Compensated Vertical Echoes. Oceans 2006, Boston, USA, 1820 September2006.

Preston, J.M., 2009. Automated acoustic seabed classication of multibeam imagesof Stanton Banks. Applied Acoustics 70 (10), 12771287.

Preston, J.M., Christney, A.C., Beran, L.S., Collins, W.T., 2004. Statistical seabedsegmentationfrom images and echoes to objective clustering. In: Proceed-ings of the 7th European Conference On Underwater Acoustics, Delft, TheNetherlands, pp. 813816.

Quester Tangent, 2002. QTC IMPACTTM acoustic seabed classication, user guideversion 3.00. Integrated Mapping, Processing and Classication Toolkit. Ques-ter Tangent, Sidney, B.C., Canada.

Sternlicht, D.D., de Moustier, C.P., 2003. Remote sensing of sediment character-istics by optimized echo-envelope matching. Journal of the Acoustic Society ofAmerica 114 (5), 27272743.

Tegowski, J., 2005. Acoustical classication of the bottom sediments in thesouthern Baltic Sea. Quaternary International 130, 153161.

van Walree, P.A., Tegowski, J., Laban, C., Simons, D.G., 2005. Acoustic seaoordiscrimination with echo shape parameters: a comparison with the groundtruth. Continental Shelf Research 25, 22732293.

L.J. Hamilton / Continental Shelf Research 31 (2011) 20002011 2011

Acoustic seabed segmentation for echosounders through direct statistical clustering of seabed echoesIntroductionThe first echo approach to acoustic seabed classificationPhysically based inverse modellingA different approach

Data and preprocessingBalls Head Bay study areaEchosounder dataEcho formationPreprocessingEffects of sampling artefacts and depth on echo shape and durationEcho selection, averaging, and energy normalisation

MethodsThe CLARA statistical clustering algorithmMethodology for the CLARA algorithm to cluster curves

AnalysisGroundtruthThe number of clustersCluster mappings and relation to groundtruth

DiscussionSome key echo characteristicsLimitations of echosounder based seabed classification

ConclusionsReferences