ICES J. Mar. Sci. 2010 McGonigle Icesjms_fsq015

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Insonification orientation and its relevance for image-based

classification of multibeam backscatter

Christopher McGonigle, Craig J. Brown and Rory Quinn

McGonigle, C., Brown, C. J., and Quinn, R. 2010. Insonification orientation and its relevance for image-based classification of multibeam

backscatter. ICES Journal of Marine Science, 67: 000000.

The use of multibeam echosounders (MBES) for mapping benthic habitat has gained widespread acceptability. Multibeam backscatter

imagery provides an objective tool for scientists and managers to chronicle the extent and condition of the benthic resource. However,

there are no standardized methods describing how best to process backscatter data to derive meaningful segmentations, although

several acquisition parameters have been identified as having the capacity to affect the classification result. This research attempts

to determine how the orientation at which a feature is insonified can affect classification outcome using commercially available soft-

ware (QTC-Multiview), and to evaluate this significance related to vessel speed as a proxy for data density. A complex 2-km2 area of

Stanton Banks, UK, was selected as the test site for the study. The area was insonified using a Kongsberg Simrad EM1002 MBES at

perpendicularly opposing orientations, at two different vessel speeds within the same 24-h period. The classifications displayed

53% (k 0.396) similarity at 4 m s21 and 49% (k 0.342) at 2 m s21 from opposing orientations. Common orientations at different

speeds were 68% (k

0.583) similar (eastwest) and 53% (k

0.384; northsouth). Most of the variation was in topographicallycomplex areas, which coincided with shallow depths (,60 m). Meteorological and oceanographic conditions at the time the data

were collected were evaluated as having had the potential to influence the outcome of the classifications. Interpretation of the

results suggests that the orientation at which insonification occurs has a greater ability to influence the classification result than

vessel speed using an image-based technique.

Keywords:backscatter, habitat mapping, image-based classification, insonification orientation, multibeam.

Received 14 August 2009; accepted 6 February 2010.

C. McGonigle, C. J. Brown and R. Quinn: Centre for Coastal and Marine Research, University of Ulster, Coleraine, Co., Derry BT52 1SA, Northern

Ireland, UK. Correspondence to C. McGonigle: tel: +44 2870 324961; fax: +44 2870 324491; e-mail: [email protected].

IntroductionSeabed classification based on backscatter data is an established

discipline with a strong geological lineage (Belderson et al.,

1972;Fish and Carr, 1990;Blondel and Murton, 1997). The use

of marine geophysical data to describe surficial seabed morpho-

logical characteristics has been common since the 1960s, although

the techniques have only diversified relatively recently into eco-

logical disciplines (e.g. Brown et al., 2002;Beaman et al., 2005).

Increasingly, marine geophysical data are being utilized for the

purposes of habitat characterization (e.g. Cochrane and Lafferty,

2002; Ojeda et al., 2004; Roberts et al., 2005; Todd and Greene,

2007). This usage has benefited benthic ecologists by equipping

them with a suite of tools with which to interrogate the seafloor

environment in a non-invasive manner, in a bid to understand

better the communities and processes acting upon them(Anderson et al., 2008; Brown and Blondel, 2009). In doing so,

marine resource management is provided with a context in

which to interpret other types of information and to develop a

solid foundation on which to make informed decisions based on

scientific evidence (Baxet al., 1999;Kostylevet al., 2003;Pickrill

and Todd, 2003;Brehmeret al., 2006).

The variety of instrumentation commonly utilized for these

applications is well described elsewhere (Lurton, 2002;Van Rein

et al., 2009). However, one of the most significant advances inhardware has been the advent of the multibeam echosounder

(MBES), which permits the simultaneous insonification of a con-

tinuous swath of seafloor in an array perpendicular to the vessel

track. This has helped to resolve the issues of across-track data

density inherent to single-beam systems, which typically have

small insonification footprints directly under the vessel track,

requiring very close lane spacing to draw meaningful conclusions

about the nature of the benthic environment. Successful

implementation of these technologies depends, however, on cor-

rection for known errors related to acquisition. Beamformed

MBES data inherently contain bathymetric information which

can, in principle, allow compensation of the backscatter to

remove angular and range dependence, but these data are them-

selves subject to the accuracy of the position and attitude data.Geometric and radiometric distortions from sensor platforms

are also a significant consideration for marine surveys (Fonseca

and Mayer, 2007), as is true for their terrestrial counterparts

(e.g. aerial photogrammetry, hyperspectral scanning, and

LiDAR). In the marine environment, these issues are further com-

plicated by the lack of common reference points for rectification

(particularly offshore), and the turbulence at the atmosphere

ocean interface can result in complex motion artefacts that are

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difficult to compensate for (Miller et al., 1997; Hughes Clarke,

2003). Advances in positioning and attitude sensors can go some

way towards mitigating these effects, but their presence can com-

promise data quality, and hence the success of any subsequent

classifications. Other common problems result from how to deal

with areas of overlap between adjacent survey lines, and the

related seams between images (Le Bas and Huvenne, 2009).

Efforts to compensate for these effects of acquisition artefacts innear real time are entering a new era of sophistication, with

recent advances in software and computer hardware (Fonseca

et al., 2009).

Irrespective of the advances, issues remain regarding the pro-

cessing, interpretation, and characterization of MBES data, and

this has not yet reached a definitive standard that is truly objective

and repeatable. Hydrographic processing of bathymetric data is

moving towards this point with developments such as the com-

bined uncertainty and bathymetric estimator, the establishment

of IHO S-57 (IHO, 2000), S-44 (IHO, 2008), and the proposed

S-100 (Greenslade e t al ., 2008). Although becoming more

common than was the case 10 years ago, the acquisition of

MBES data is still a (relatively) expensive undertaking, and it is

much less common when the motivation is exclusively ecological(Foster-Smith and Sotheran, 2003). This often means that biol-

ogists/ecologists who utilize acoustic data may be working with

data that have been acquired for other purposes. In contrast, the

processing of backscatter data has not yet received a comparable

level of attention. The backscatter component of MBES data is

often regarded as an added-value product, and not a significant

consideration in its own right. As is often the case, gain settings

during acquisition are optimized for the collection of bathymetric

data, and areas where this cannot be effectively accounted for

result in reduced utility for habitat characterization using back-

scatter data. This disparity has been described previously

(Fonseca and Mayer, 2007).

Efforts to develop effective backscatter interpretation tech-

niques have come from both academia and the commercialsector. Some of the principal academic developments are broadly

divisible into textural (de Moustier and Matsumoto, 1993;

Blondel et al., 1998; Le Bas and Huhnerbach, 1999; Huvenne

et al., 2002; Cutter et al., 2003) and angular response (Hughes

Clarkeet al., 1997;Fonseca and Mayer, 2007). The angular range

analysis developed byFonseca and Mayer (2007) has moved into

the commercial sector and has recently been incorporated as a soft-

ware tool in several commercially available MBES data-processing

packages (Mayer, 2009). Much of the developmental effort has

focused on the production of an image as free as possible from

error on which to make an interpretation, and/or conduct further

analysis. Hence, a significant obstacle to successful classification

is the inability to remove acquisition artefacts from the raw back-

scatter data in a post-processing environment and how to generatemore value from single-band greyscale images.

One of the main commercial products that allow production of

an objective MBES backscatter classification is Quester Tangent

Corporations (QTC) Multiview (Preston et al., 2001; Collins

and Preston, 2002). The software QTC-Multiview has been

demonstrated in the literature to have performed effective segmen-

tations (Robidoux et al., 2008; McGonigle et al., 2009, 2010;

Preston, 2009). The software performs analysis on the image-based

components of MBES data. Effectiveness in the classification

depends on effective range compensation of the original input

data, the motion referencing unit, the sensor array used in

conjunction with the MBES, and quality control guided by the

user. In aspiration, the principal attraction of the software is that

it attempts to go a step further than the generation of a compen-

sated image, working instead towards the objective segmentation

of the image based on individual survey lines.

Rationale and aims

Multibeam datasets are increasingly being used as baseline infor-mation on which to formulate objective management decisions

(Kostylev et al., 2001; Pickrill and Todd, 2003). These data are

used commonly to inform marine spatial planning and resource

management from regional to national levels. Various initiatives

undertaken in recent years have demonstrated the validity of this

approach to benthic resource management (e.g. Mapping

European Seabed Habitats; Irish National Seabed Survey).

The role of acquisition parameters is particularly important

when marine surveys are conducted for the purposes of environ-

mental monitoring, such as change detection (Coppin et al.,

2004; Lu et al., 2004), using multi-temporal and/or multisystem

time-series (Hughes-Clarke et al., 2008). Ineffective data con-

ditioning can result in the production of substandard baseline

information for classifications, leading to heightened potentialfor misinterpretation of acquisition artefacts, equally true for

both manual and automated analysis techniques.

Previous research undertaken at the site occupied here has

demonstrated the significance of data density as a consideration

for image-based classification (McGonigle et al., 2010). There

may be other differences between classifications, caused as a

result of natural changes in the system, variations in the way the

data are collected (e.g. operational frequency, sample interval,

orientation of insonification), or localized changes that will

affect the way in which the system is observed (e.g. sea state).

The difficulty arises as a result of trying to determine which

factor is responsible for the variation(s) observed and which is

incidental. Therefore, to maximize the validity of comparisons,

efforts need to be made to keep such variations to a minimum(McGonigle et al., 2010). Developing from these initial investi-

gations, the work presented here aspires to examine the effects

of insonification orientation on the results of image-based

classification.

Our research aims to determine whether the orientation at

which an area of seabed is insonified influences the results of com-

parably conditioned classification performed using commercially

available software. In doing so, we hope to demonstrate the signifi-

cance of insonification orientation as a consideration for the

acquisition of MBES data to be used for image-based processing.

Specific research objectives include:

(i) quantification of the agreements observed between classifi-

cations conducted at perpendicularly opposing orientations;(ii) evaluation of the agreements in the context of classifications

obtained from similar orientations at different vessel speeds;

and

(iii) outlining the principal concerns associated with acquisition

of MBES data for the purposes of image-based classification.

MethodologyThe study area is a subsection of the Stanton Banks feature, pre-

viously investigated by McGonigle et al . (2009, 2010). The

Page 2 of 14 C. McGonigleet al.


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feature is an offshore granitic outcrop in the MalinHebrides sea

area and represents a southern extension of the Outer Hebrides

Lewisian gneiss formation (Fyfe et al., 1993). The Banks have a

complex surficial expression, characterized by heavily fissuredbedrock surrounded by large tracts of unconsolidated sediments.

The area shoals at 60 m water depth around the summit of

the reef structure, descending to the surrounding depths of

around 200 m on the continental shelf (Eden et al., 1971). The

Stanton Banks are situated in the UKs exclusive economic zone,

located 100 km west of Scotland, 30 km north of 568N

(Figure1). The Banks have been investigated over the course of

the past decade (Robertset al., 2005; Brown and Blondel, 2009;

McGonigle et al., 2009,2010), owing to their physical complexity

and the lack of well-described rocky reef on the continental shelf in

UK territorial waters. The area was revisited between 12 and 14

December 2008 specifically for the purposes of this research.

Data acquisition and processingIn all, 14.5 h of MBES data (including turns) were collected over a

2-d period by the Irish Marine Institute aboard RV Celtic

Explorer. Through the course of previous research, a 2-km2

area was identified as being indicative of the acoustic and biologi-

cal variation evident at the site (McGonigleet al., 2010). That area

was surveyed from perpendicularly opposing orientations (east

west, EW; and northsouth, NS) at two different speeds (4 and

2 m s21), resulting in four complete insonifications (Figure 2).

The trackline orientations conducted form the basis for the file

naming conventions used (i.e. EW, NS, and EWNS, composite

of both orientations at a common speed). The lane spacing was

nominally maintained at 0.25 km, which allowed for a corridor

of 0.5 km swath width to be insonified at each pass, with 50%

overlap between adjacent lines. This spacing was based on a

Figure 1. Location map of the study area in the context of the Stanton Banks feature. The location of the UK Met Office K5 databuoy isincluded, as are the 100-, 500-, 1000-, and 2000-m isobaths derived from the GEBCO 1 arc minute bathymetric grid.

Figure 2. Navigation tracklines for survey CE0803. The blackbounding box in the centre of the figure is the 2-km2 area A, thedata from which form the basis for subsequent classifications.Perpendicularly opposing survey lines were undertaken at twodifferent speeds (4 and 2 m s21), as highlighted.

Insonification orientation and image-based classification of multibeam backscatter Page 3 of 14


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theoretical flat-bottom assumption, and as such, there were vari-

ations depending on water depth. However, variation in overlap

was determined to be preferable to varying the angular sector

for the purposes of the classification. Pulse length was maintained

at 0.2 ms, operational frequency was 95 kHz, ping rate was 1 Hz,

and the angular sector was maintained at 1308.

After acquisition, bathymetric data were cleaned in CARIS

HIPS v.5.3 (Universal Systems Ltd) and gridded with a 5 m

5 m bin size. The beam time-series backscatter data were output

from QTC-Multiview at 1 m 1 m pixel size, with greyscale

values representing backscatter intensity. These values were

output in generic ASCII format (x,y, b) after being compensated

for range and bathymetric variation by the software. To balance

between processing speed and derived surface quality, the grids

were created using a weighted moving-average algorithm with a

weighted diameter of three. Slope was obtained from the bathy-

metric surface as a derivative product using the Spatial Analyst,

extension of ArcMap v. 9.2. Summary statistics (min, max,

mean, and standard deviation) were generated for bathymetry,

slope, and backscatter.

Seabed classificationThe initial classification performed by QTC-Multiview is based onthe extraction of 132 variables from user-specified rectangular

patches of the backscatter imagery. This process has been

described in detail elsewhere (Robidoux et al., 2008; McGonigle

et al., 2009,2010;Preston, 2009), but for reasons of clarity, it is

briefly outlined below.

The sonar data presented for classification were initially sub-

jected to compensation to minimize the effects of range and

angular dependence. At that stage, ASCII output files of the com-

pensated backscatter were generated at user-specified resolutions

(COMPEX) for creating mosaics in third party software. The

data were then quality-controlled either using imported data

flags (e.g. CARIS or HYPACK), internal tools (range masking,

beam rejection, and depth threshold), or a combination of bothapproaches. Physical dimensions of the patches were user-defined,

selected from a choice determined by the number of pixels in the

backscatter image (QTC, 2005).

The centrepoint of each patch inherited the 132 variables,

which were subsequently reduced to three values (Q-values) by

principal component analysis (PCA). Until that point, each line

was processed independently; all subsequent processing was per-

formed on merged full feature vectors (FFVs), which were

derived by the application of a suite of algorithms to the backscat-

ter imagery from the individual survey lines (QTC, 2005).

Ordering the data into the optimum number of groups was

achieved by the use of the softwares automatic clustering engine

(ACE), which functions by application of a simulated annealing

k-means algorithm to the PCA-reduced data in three-dimensionalvector space (QTC, 2005). Acceptance of one of the hypotheses

generated by ACE allowed us to output the data as a final classified

vector file containing position, depth, the three Q-values, associ-

ated confidence and probability, and various ancillary fields

(QTC, 2005). Interpolation of the classified vector dataset was

the final stage in the classification procedure. That function was

performed using the stand-alone categorical interpolation

package QTC-Clams, the functionality of which has been well-

described previously (McGonigle et al., 2009).

In summary,the output data products from theQTC-Multiview

and -Clams processing environment for each survey are threefold:

(i) the compensated backscatter as greyscale values (0255) at

user-specified bin size (.tif), (ii) the final classified vector

dataset (.seabed), and (iii) the categorically interpolated classified

surface (.grd).

For all geophysical data, acquisition and post-processing were

conducted in as similar a manner as possible. CARIS reject flags

nominated by the hydrographer were imported into

QTC-Multiview, and a common depth threshold filter (500) wasapplied to all survey lines. The compensated backscatter was

then exported at 1 m bin size (as .COMPEX file). The rectangle

dimensions for backscatter sampling were common in the

number of pixels for all surveys at all orientations, although (as

intended) the variations in vessel speed resulted in significant vari-

ation in the rectangular patch footprint on the ground. The actual

footprint sizes for 129 pixels 9 pixels were 8.0 m 37.0 m at

4 m s21, and 8.0 m 18.5 m at 2 m s21. The rectangle centre-

points (FFVs) were subjected to further quality control before

classification by evaluating bathymetric outliers in the editing

module in QTC-Multiview. Spatial and temporal filters were

applied to the records to include only those FFVs whose positions

were within the specified 2-km2 area (Figure2).

Usingthe approach describedabove, a seriesof uniqueFFVs wasgenerated for each of the two survey orientations at each of the two

vessel speeds to ensure that each classification was independently

valid. Additionally at this stage, further datasets were created by

merging the FFVs of opposing orientations at common speeds,

leading to the creation of composite datasets which could be inde-

pendently classified. This resulted in a total of six classifications:

EW, NS, and EWNS at the two speeds (2 and 4 m s21). Before the

final classification, the optimum numbers of classes defined by

ACE were accepted for all survey orientations and speeds, and the

data were processed to their logical conclusion. The final classified

outputs from QTC-Multiview were subsequently processed

through QTC-Clams at standard settings to provide a basis for

comparative evaluation. The node spacing for the interpolation

in QTC-Clams was maintained at 5 m, and the interpolationsearch radius was set to 150 m, taking the 10 neighbouring

points closest to the centre of each grid node into account for

majority weighting.

Owing to significant variations in the ACE optimum number

of classes identified, it became necessary to refine this procedure

further. All classifications were iteratively re-examined, and the

maximum number of classes identified by ACE across all classi-

fications was imposed on the remaining datasets to form a basis

for comparison. The rationale for processing in this manner was

based on the assumption that with larger numbers of classes, the

significance of any omission was determined to be minimized.

This common maximum was selected before the classifications

were manually refined into a smaller number of more meaning-

ful classes based on their common geographic and three-dimensional vector-space occurrence. It is suggested that proces-

sing in this manner would maximize the expression of physically

smaller classes in each classification and is the most robust

manner of proceeding with comparison of the classification

results based on previous work presented by McGonigle et al.

(2009).

Seabed classification data analysis

The data formats were conditioned to facilitate assimilation into a

geographic information system. Generic data formats (.csv) were

converted to software-specific (.shp) for ArcMap v. 9.2. The data



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were projected to UTM Zone 29 N (WGS 1984 Datum) to allow

metric proportioning to be maintained throughout the analysis.

The grid-conversion process was performed using Geospatial

Designs GridConvert freeware (Evans, 2005). ESRIs Spatial

Analyst extension was then used to calculate the average difference

between the four bathymetric surfaces, derivative slopes, and

COMPEX backscatter images. Percentage agreements (fraction

correct) and associated kappa statistics between classified surfaceswere obtained by re-classifying the class numbers to a common

schema based on their geographic occurrence and cross-tabulating

the surfaces. These common areas within each of the classifications

were masked, and values were calculated for the remainder using

Map Comparison Kit (Hagen, 2002; Visser and Nijs, 2006).

Kappa statistics for the class similarities were calculated using

Cohens kappa, based on the formula

k=P(A) P(E)

1 P(E),

whereP(A) is the original fraction of agreement, and P(E) is the

expected agreement based on random location subject to

the observed distribution (Hagen, 2002). The results for each ofthe 15 possible combinations of the six classifications were com-

piled in a similarity matrix with accompanying k-statistics.

Attitude data

Vessel positioning, attitude (pitch, roll, heave), and heading were

recorded by Kongsberg Seatexs Seapath 200. The systems inertial

referencing unit (IMU) optimally has positional accuracy of 0.7 m

root-mean-square (RMS) error or 1.5 m (95% circular error prob-

able). Attitude data have pitch and roll accuracies of 0.038(RMS)

for +58 amplitude, heave accuracy is 5 cm or 5% (whichever is

greater), and heading has 0.058 RMS (Kongsberg Seatex, 2001).

Calibration using sound velocity profiles was recorded before the

start of each survey using an Applied Microsystems SVPlus witha time-of-flight accuracy of +0.03 m s21. Attitude data were

exported from CARIS HIPS in ASCII format and visualized

using Golden Softwares Grapher v. 5.01.

Ancillary data

The most proximal oceanographic data platform at the time of the

survey was the Met Offices Marine Automatic Weather Station

Network (MAWS) K5 databuoy, relevant data recorded from

which included hourly measurements for windspeed, wind direc-

tion, measured wave period, and measured wave height. K5 is

365 km northwest of the study site (Figure 1). Databuoy K4,

335 km southwest of the site, was not operational at the timeof the survey. The nearest meteorological observatory at the time

of survey was the Tiree automatic coastal weather station,

80 km northeast of the site (Figure 1). Useful data from that

station included mean windspeed and direction measured

hourly. The hourly observations from both platforms were visual-

ized using Golden Softwares Grapher v. 5.01. General synoptic

conditions were obtained retrospectively from the UK Met

Office for the Malin, Hebrides, and Bailey shipping areas, along

with the inshore forecast for the area from Ardnamurchan Point

to Cape Wrath (including the Outer Hebrides) for the week the

survey was conducted (1017 December 2008), shown in Figure 1.

ResultsMBES data

The results of the MBES surveys presented include the summarystatistics of bathymetry, slope, and backscatter values observed

in the 2-km2 area at the predefined orientations and speeds

(Table 1), and demonstration of where the variations occur,

based on the average difference between surveys (Figure 3).

The physiographic conditions for the Stanton Banks area have

been described elsewhere (McGonigle et al., 2009). In that 2 km2

subsection, the conditions are typified by a shallow gradient

running from northwest to southeast, bounded on either side by

heavily fissured bathymetric highs around 260 m water depth

(Figure3a and b). These fissures do not adhere to a clear direc-

tional trend. The central downward-sloping basin is punctuated

by clear linear transitions perpendicular to the direction of greatest

slope (Figure 3b), which are further evident in the backscatter

(Figure3c).The survey results show insignificant variations between the

bathymetric surfaces and their derivative product (slope). The

values presented (Table 1) demonstrate that the mean depths

observed range over ,1 m, between 2118.13 and 2117.87 m.

Similarly, the standard deviation for depths ranged over

,0.05 m, between 27.95 m and 27.91 m. Figure 3a shows those

areas where the variations are focused by presenting the average

differences between surveys (+2 m). The features are most

evident around the fissures in the bathymetric highs in the south-

western part of the survey area. Slope values derived from the

bathymetric grid show a similarly low maximum variation

around the mean and a low standard deviation between surveys

(,0.2 m). Mean values for slope range between 7.098 and 7.268,

with standard deviations of 7.518 and 7.668 (Table1). However,there are localized variations evidenced by isocontours in

Figure 3b (+58). Similar to the bathymetric variations, these

tend to be focused around the fissures, and around the linear gra-

dients orthogonal to the trend of slope in the central basin

(Figure3b).

Texturally, the area has a strong transitional gradient of back-

scatter intensity in the basin, between the bathymetric highs on

either side. At extremes of the greyscale range, low backscatter is

principally concentrated in two main areas, first in the northeast-

ern section of the basin, from which its extent expands in a south-

eastern orientation. In the second instance, it is found in several of

Table 1. Summary statistics of gridded bathymetry (BY) as definedby CARIS cleaned soundings (5 m), slope (SL) as derived from thegridded bathymetry, and greyscale backscatter imagery (BS) asdefined by QTC-Multiview COMPEX output (1 m).

Survey Minimum Maximum Mean s.d.

EW 4 m s21 BY (m) 2186.98 256.79 2118 27.95

EW 2 m s21 BY (m) 2187.71 256.72 2118.13 27.93

NS 4 m s21

BY (m) 2

187.29 2

56.65 2

117.87 27.93NS 2 m s21 BY (m) 2185.86 256.38 2117.9 27.91

EW 4 m s21 SL (8) 0 60.92 7.11 7.51

EW 2 m s21 SL (8) 0 61.28 7.09 7.54

NS 4 m s21 SL (8) 0 62.07 7.21 7.57

NS 2 m s21 SL (8) 0 71.68 7.26 7.66

EW 4 m s21 BS (pixel value) 0 255 69.5 31.22

EW 2 m s21 BS (pixel value) 0 255 72.26 24.2

NS 4 m s21 BS (pixel value) 0 255 66.25 31.98

NS 2 m s21 BS (pixel value) 0 255 67.7 22.92



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the recesses of fissures in the central western section of bathymetric

highs. The largest concentrations of high backscatter are also in

proximity to these features. Figure 3c shows one patch in the

southeastern extremity of the western bathymetric highs, and

another similarly just below the high in the northeast.

Remaining localized areas of high backscatter intensity are in

and around the surficial fissures on the western bathymetric

highs (Figure3c).

The variations in backscatter intensity observed between

surveys are pronounced, so the results are presented in theirentirety below (Figure 4). However, the summary statistics pre-

sented in Table 1 show that the means range from 66.25 to

72.26 greyscale pixel values (6.01 pixels). Similarly, the standard

deviations range between 22.92 and 31.22 greyscale pixel values

(8.3 pixels). The variation is further exemplified by Figure 3c,

which shows the isocontours of average difference between

surveys. The contours presented (+70 pixels of a possible 140

around zero) show that there is significant variation even

beyond that level of difference. Similar to the data presented

above, the variation is concentrated around the fissures

(Figure 3c), although a substantial new area is evident in the

central portion of the high backscatter area in the east.

Simultaneous comparison of the compensated mosaics is necess-

ary to illustrate this point.A more comprehensive exposition of the differences between

the backscatter mosaics is presented in Figure 4. There are

strong range-dependent artefacts in all surveys, although there

has been interpolation in areas of overlap between adjacent

survey lines. This amounts to a crude averaging in these areas.

No-data areas have been preserved, so where insufficient data

exist, no interpolation has been performed. The no-data areas in

each survey account for 6.05% (EW 4 m s21), 0.99% (EW

2 m s21), 8.32% (NS 4 m s21), and 1.08% (NS 2 m s21) of the

total surface area in the 2-km2 area. Despite this, there are

marked differences between survey results. The occurrence of

regular linear artefacts in the across-track (range) persists in the

compensated backscatter data, although data input to this

process have been averaged across the overlapping regions.

Moreover, the intensity of the striping appears to be more

clearly defined in Figure4c and d (representing the NS surveys,

at both speeds), and that of all interpolated images, this effect is

most pronounced in Figure 4d (NS 2 m s21 survey).

Unsurprisingly, for the resolution at which the images were pre-

pared (1 m bin size), most no-data areas were found in the

4 m s21

surveys. However, of further interest, the remaining(and 2 m s21) no-data regions appear to be concentrated around

the westernmost bathymetric high close to the nadir of the

tracklines (Figure4). Reasons for this may include the omission

of data from the CARIS reject flags, the presence of acoustic

shadows, or the application of the depth threshold filter in

QTC-Multiview.

Seabed classification

The optimum number of classes identified in the QTC-Multiview

output file (.seabed) was determined by the ACE classification pro-

cedure. In the first instance, the number of classes identified by

ACE varied substantially between surveys, from 8 classes (in

EWNS 2 m s21) to 14 classes (in EWNS 4 m s21 and NS

2 m s21

). Rather than forcing the more complex classificationsto conform to the smaller numbers of classes, the data were reclas-

sified to the maximum number observed for any survey

(14 classes). This helped to ensure consistency in the expression

of common features evident in the backscatter. These classified

data were merged into seven classes based on their common geo-

graphic and three-dimensional vector space occurrence and reclas-

sified to a common numeric scheme (1 7) to facilitate empirical

comparison in a similar manner to that presented by McGonigle

et al. (2009).

The QTC-Clams interpolated classified surfaces for the seven

merged classes are shown in Figure5. The continuity of features

Figure 3. (a) Bathymetric grid (5 m) of the survey area, determined by CARIS cleaned soundings from the EW 2 m s21 survey. Superimposed

contours are the average bathymetric difference between the six permutations of the four surveys. (b) The bathymetric slope derived from (a).Contours represent the average difference between surveys. (c) Mosaic of greyscale backscatter as determined by QTC-Multiview line-by-lineCOMPEX output (1 m grid). Contours represent the average backscatter difference between surveys.



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is readily apparent from visual comparison, as are many subtle

variations between the surfaces. This is particularly true about

classes 1, 3, and 4, whose features appear to be largely consistent,

and less so for classes 2, 5, 6, and 7. Smaller fragments of classes

have been compromised by the interpolation process, and such

classes are frequent in the western section of bathymetric

highs (Figures 3 and 5), particularly in the NS and composite

classifications (EWNS).

A similarity matrix of all 15 permutations of the six indepen-

dent classifications is presented in Table 2, along with the kappa

statistics describing the significance of each individual similarity.After examination of the geographic distribution of the classes

(Figure 5a f), the most obvious overall similarity is between

common orientations at different speeds (e.g. Figure 5a and d).

This is further qualified by the percentage agreements and kappa

statistics presented in Table 2. In general, there is a reasonable

agreement between datasets, with only two falling below 50%,

and in both cases, the agreements are related to the NS 2 m s21

survey (Figure 5f). The kappa significance of this agreement in

both cases is described as fair (Landis and Koch, 1977). There

are several such instances of fair agreement, i.e. where k 0.21

0.40, in the similarity matrix, all of which are related to the

comparison of categorical images against the NS classifications.

These effects are consistent at both vessel speeds.

This suggests that the two separate NS classifications are less

closely related (53% similar; k 0.384) than the 68% similarity

(k 0.583: moderate agreement) between the EW classifications,

observing the same variations in vessel speed (Landis and Koch,

1977). When similarities are compared based on a constant

speed at opposing orientations, the results at 4 m s21 are 53%

similar (k 0.396), in contrast to 49% at 2 m s21 (k 0.342),

although both agreements can only be described as fair at best

(Table2; Figure5;Landis and Koch, 1977). As would be expected,although independently classified, the composite datasets (EWNS)

have similarity values intermediate of their constituent classifi-

cations (Figure5b and e). This is reflected in the similarity and

kappa statistic values presented in Table2.

When examined in a geographic context, it is apparent that the

areas of maximum variation between classifications are concen-

trated around the reef areas in the southwest and northeast of

the study area (Figure5). The variation appears as more fractured

occurrence of classes 2, 3, 5, and 6 in this western section of

both NS classifications. Discounting the composite classifications,

the most fragmented of the remaining four appears to be the

Figure 4. Mosaics of backscatter imagery obtained using QTC-Multiview COMPEX output interpolated greyscale backscatter. Decibel levels

are normalized through this process to 256 greyscale values. Mosaic from orientations: (a) EW at 4 m s

21

, (b) EW at 2 m s

21

, (c) NS at 4 m s

21

,and (d) NS at 2 m s21.



8/14

NS 2 m s21

classification (Figure5f), where the main area of thereef (classes 5 and 6) has lost most of its boundary definition.

This issue presents itself again in the 2 m s21 composite

(EWNS) classification, undoubtedly as a result of the same input

data, although the presence of class 2 in the main area of fragmen-

tation has been largely diluted by the inclusion of the EW 2 m s21

survey data.

Attitude data

The motion-referencing data recorded by the Seatex 200 are pre-

sented in summary in Table 3. Variations in pitch experienced

during the surveys were minimal, with mean values per

survey from 9.338 to 12.538, with standard deviations of 1.468and 1.978, respectively (Table3). Roll variations between surveys

were more clearly demonstrated. The mean values for EW lines

are 12.798and 12.358, respectively, with 2.108and 2.058standard

deviation. In contrast, the NS surveys were markedly greater, with

.208 means and .38 standard deviation as average values

(Table3). Heave showed similar results for the NS surveys, increas-

ing to a 9.28 average from 6.78 for the mean of the EW surveys

(Table 3). The extent of that variation is shown in Figure 6,

which is a 10-min time-series of attitude data from two survey

lines conducted on opposing orientations. The variation in ampli-

tude is most pronounced in the roll data (Figure 6).

Figure 5. QTC-Multiview output classifications processed through the categorical interpolation software QTC-Clams. The grids have had abinary mask applied to reduce their physical extents to a common area. The classifications were produced from FFVs generated by surveyorientations of (a) EW at 4 m s21, (b) EWNS at 4 m s21 (composite), (c) NS at 4 m s21, (d) EW at 2 m s21, (e) EWNS at 2 m s21 (composite),and (f) NS at 2 m s21.

Table 2. Matrix of percentage similarities between interpolated classified surfaces presented in Figure 5.

Survey EW 4 m s21 EW 2 m s21 EWNS 4 m s21 EWNS 2 m s21 NS 4 m s21 NS 2 m s21

EW 4 m s21

EW 2 m s21 68 (0.583)

EWNS 4 m s21 67 (0.569) 64 (0.531)

EWNS 2 m s21 58 (0.461) 62 (0.508) 58 (0.454)

NS 4 m s21 53 (0.396) 52 (0.386) 61 (0.501) 56 (0.425)

NS 2 m s21 48 (0.334) 49 (0.342) 51 (0.365) 60 (0.472) 53 (0.384)

Similarities were determined by cross-tabulating the individual survey grids on a cell-by-cell basis using ArcMap v. 9.2 Spatial Analyst extension, with kappastatistics (in parenthesis) derived using the Map Comparison Kit (Hagen, 2002).



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Ancillary data

Oceanographic and meteorological conditions were obtained from

the Met Office MAWS K5 databuoy and the Tiree automatic

coastal weather station, as the closest mainland and moored plat-

forms at the time of survey (Figure 1). The K5 data were recorded

in the Bailey sea area, although the forecast conditions in Malin

and Hebrides were comparable in wind strength and direction

(Figure7). Conditions at the time of the EW surveys (Figure 2)

are evidenced by Figure 7a and c, where the cyclonic nature of

the variation in wind direction is evident throughout all direc-

tional quarters. The measured wave height and period

(Figure7e) do not have a directional component, although infer-

ence can be made that the wind direction had resulted in a con-

fused sea state at that time. The Met Office forecast sea state forthis and all adjacent areas was described as rough or very rough.

The change in conditions at the time of the NS surveys is evi-

denced by Figure 7b and d, where the wind direction at K5

(Figure 7d) is consistently from the southwest to the northwest

quarter, and the greatest proportion of the wind was of strength

6 m s21. The measured wave heights and periods do not

appear to be significantly different from Figure7e. Although the

data do not have a directional component, the inference is made

that it was common to the wind direction from K5 (Figure7d).

The Met Office forecast sea state for Malin, Hebrides, and Bailey

then was very rough or high (Figure 1).

DiscussionInsonification orientation and vessel speed

Using established methods for the comparison of categorical

remotely sensed data (Hagen, 2002; Coppin et al., 2004; Visser

and Nijs, 2006), marked differences have been demonstrated

between classifications produced from a common 2-km2 area

when insonified from perpendicularly opposing orientations.

Based on the data presented, most of the variation between classi-

fications was in the western bathymetric highs with topographi-

cally complex surfaces (Figure 3). The greatest disagreements

with the weakest kappa statistics were those based on comparison

with the classification resulting from the NS 2 m s21 survey. In this

respect, the NS 2 m s21 survey could be regarded as the most dis-

similar of the four surveys. Without doubt, this matter is inextric-ably linked to the conditions at the time of survey (Figure 6b),

although that statement in itself draws attention to a very signifi-

cant factor from the perspective of ecosystem monitoring, which is

of particular relevance to government agencies.

The differences related to common orientations at different

vessel speeds are less pronounced than similar vessel speeds at con-

trasting orientations. Therefore, and at the conditions and speeds

tested, variations in insonification orientations resulted in more

significant differences than vessel speed in the QTC-Multiview

classification process. This supports similar studies conducted

using single-beam echosounders (Hamilton et al., 1999; Brown

Figure 6. A 10-min sample of attitude data for pitch (8), roll (8), and heave (m), representative of conditions observed during (a) the EW4 m s21 survey, and (b) the NS 2 m s21 survey.

Table 3. Summary attitude data from the Kongsberg Seatexs Seapath 200 motion-referencing sensor, with the mean range representingthe mean of the data ranges for all individual lines within each survey.

Survey Pitch [88888; mean range (s.d.)] Roll [88888; mean range (s.d.)] Heave [m; mean range (s.d.)]

EW 4 m s21 9.58 (1.50) 12.79 (2.10) 6.79 (1.12)

EW 2 m s21 12.53 (1.97) 12.35 (2.05) 6.68 (1.16)

NS 4 m s21 10.00 (1.46) 21.93 (3.42) 9.28 (1.47)

NS 2 m s21 9.33 (1.46) 20.50 (3.50) 9.18 (1.51)



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Figure 7. Ancillary meteorological and oceanographic data from the UK Met Office. (a) Windspeed (m s21) and direction (8, true) at Tireeautomatic coastal weather station (Figure1) during EW surveys. (b) As for (a), but during NS surveys. (c) As for (a), but at the K5 databuoy. (d)As for (b), but at the K5 databuoy. (e) Measured wave height (m, indicated by left axis and the bar chart) and measured wave period(s, indicated by the right axis and the line graph) at the K5 databuoy during EW surveys. (f) As for (e), but during NS surveys.



11/14

et al., 2005), some of which showed that vessel speeds between 1.5

and 6 m s21 did not significantly influence the results of seabed

classification when using a similar QTC classification method

(von Szalay and McConnaughey, 2002). This does not imply

that there is less detailed information in the slower survey, only

that the method of classification is not sufficiently refined to be

influenced by the relative increase in data density (McGonigle

et al., 2010). Consequently, we propose that vessel speed shouldbe optimally matched to conditions to ensure the most effective

balance between vessel stability, data coverage, and resolution.

The role of survey conditions

The significance of the conditions at the time of survey cannot be

underestimated in terms of the impact they can have on the results

of any analysis performed on the data acquired. Despite the short

turnaround time between the surveys, there was a significant

change in conditions, as evidenced by the data presented in

Figures6 and7. Although the information depicted in Figure 7

is largely contextual, when viewed in conjunction with the attitude

data (Figure 6), it adds more comprehensive detail to the con-

ditions observed on site at the time of survey. The 10-min time-

series of attitude data from the motion-referencing unit(Figure6a and b) attests to the variations over the course of the

24 h period in which the data were collected. The evident increase

in vessel motion in the NS surveys, most notably roll, would be

expected to have profound implications for data quality, because

the transducer array is mounted athwartships. There is also signifi-

cant evidence of this in the backscatter mosaics of the NS surveys,

where there is across-track striping, most notably in the 2 m s21

survey. This is, however, suggested to be an unavoidable conse-

quence of surveying offshore in temperate latitudes, at any time

of the year. These variations, though regrettable, are not unex-

pected and would very likely be shown if this experiment was

repeated. Presentation of the results does, however, illustrate a rea-

listic scenario that highlights issues about the repeatability of

science at sea. Hence, these issues need to be taken into accountwhen surveys are conducted; they have obvious implications for

habitat monitoring, change detection, and acoustic remote-

sensing in the marine environment.

Implications for other researchers

In the QTC-Multiview classification process, topographically

complex features appear to be more susceptible to the effects of

insonification orientation. Therefore, when surveying large areas

that are topographically complex, it would be beneficial to try

and keep the survey lines to a parallel or sub-parallel orientation

to ensure consistency within the subsequent classification.

Crosslines obtained for the purposes of conforming to IHO

orders should be omitted from the classification. If the survey is

being conducted exclusively for the purposes of classificationusing QTC-Multiview, the results suggest limited benefit in redu-

cing speed below 4 m s21. This may, however, be due to the iner-

tial instability of the acquisition platform relative to survey

conditions, rather than an increase in along-track data density.

Bearing this in mind, it is suggested that it may also be beneficial

to cover the same area from orthogonally opposing orientations,

because doing so would allow greater flexibility in terms of the

original configurations of rectangle dimensions, resolving poten-

tial imbalance in the distribution and density of classified FFVs.

In a recent study,McGonigle et al. (2010) demonstrated the sig-

nificance of the FFV dimensions relative to the search radius of

the interpolation mechanism. If there are disparate dimensions

in either the along- or across-track domain of the FFV dimensions,

the resulting imbalance in data density cannot be reconciled ade-

quately using a circular interpolation search radius.

Clearly, acquiring comparable data densities in along- and

across-track domains only serves to benefit the shortcomings of

the interpolation process and is suggested to be of limited value

with regard to the effectiveness of the classification process(McGonigleet al., 2010). With regard to orientation, we believe

that insonification of features for the purposes of image-based

classification of backscatter should be conducted in a manner

that would minimize the effects of dynamic motion artefacts

(Milleret al., 1997;Hughes Clarke, 2003). Perhaps the orientation

of survey lines should be directed such that the predominant direc-

tion of the sea state is fore or aft of the vessel, or as close to that as

practically possible, i.e. parallel to the transducer array. If it is

beyond the capacity of the acquisition platforms motion sensors

and classification software to correct for these factors, then the

resulting classification should be used with caution.

Limitations of the approach

The ACE clustering method, which is detailed more explicitly byPreston (2009), identified markedly different numbers of classes

between surveys and did not provide a basis for direct comparison

between classifications, supporting the findings ofMcGonigleet al.

(2009,2010).Robidouxet al. (2008)similarly concluded that the

optimum defined number of classes may not be the best choice for

the classification accepted. In QTCs product literature, it is stated

that other criteria such as system artefacts may be present in the

optimal result (QTC, 2005), and hence necessitate the selection

of an alternative number of classes. The findings of this research

support this premise. Manual clustering within the software was

investigated using a method described byFreitas et al. (2003a,b)

and (QTC, 2005). However, it was not suitable for comparison

between separate classifications, because the optimal splits

between clusters were observed on different axes in each classifi-cation, and the suggested methods for indicating optimal split

levels similarly yielded different results.

Another problem previously identified with the QTC method is

related to data density of FFVs, and the lack of a more sophisti-

cated search radius for interpolation (McGonigle et al., 2010). In

the classifications performed using composite datasets

(Figure 5b and e), this issue was resolved by having a balanced

density of FFVs in both track orientations, although this was

still a consideration for the single-orientation classifications

(EW and NS).

Issues relating to overlap and the boundaries of adjacent lines

in swath bathymetry are particularly relevant for seabed classifi-

cation with QTC-Multiview, because the classification is per-

formed line by line, so data from these regions are included inthe classification process. Therefore, conflicting classifications

can arise if the same area is insonified in several passes. The

general significance of overlap is discussed more fully elsewhere

(Hughes Clarke et al., 1997; Miller et al., 1997; Fonseca and

Mayer, 2007;Le Bas and Huvenne, 2009). This issue is resolved

to a certain extent by the interpolation process in QTC-Clams,

but as discussed, this presents additional challenges to successful

classification.

There is undoubtedly a lesser signal-to-noise ratio in both

the NS surveys than the EW, and to an extent this complicates

the issue of comparison. However, variations in sea state and



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weather conditions between surveys are a realistic and unavoidable

consequence of surveying in such an exposed location. It was

anticipated that the comparisons between the two NS surveys

would provide a basis for comparison with those of an EW orien-

tation owing to the low temporal disparity between the datasets

(,24 h). This has been achieved as far as possible in respect to

the conditions at the time of survey, and the variations in con-

ditions have been presented and taken into account (Figures 6and 7). Moreover, it was anticipated that the 2 m s21 survey

would be more effective at resolving seabed features and bound-

aries than the 4 m s21 one, although this has not been convin-

cingly demonstrated by the results presented. In summary, it is

suggested that the results of this research effort may be approach-

ing the limit of what is achievable using this technique.

Conclusions

Our research has demonstrated that the orientation at which a

feature is insonified can significantly influence the outcome of

classifications performed using commercially available software.

Insonification orientation is clearly a relevant consideration for

the acquisition of MBES data for image-based classification of

backscatter. The level of agreement between classifications orig-inating from perpendicularly opposing insonifications has been

quantified, and shown to have more of an effect than data

density caused by a 50% reduction in vessel speed. The role of

survey conditions has been examined and discussed as a potential

confounding variable. From a management perspective, this has

important implications where repeat surveys are conducted for

the purposes of environmental monitoring and change detection.

Understanding how the decisions made during acquisition can

effect subsequent classifications is crucial, and can better equip

researchers and government scientists to make sounder decisions

than if they were made without such understanding.

Some of the limitations of our research could have been over-

come by a simplification of the objectives or a modification of the

experimental design, such as by repeating the same courses at thesame speeds. Comparison of classifications obtained in that

manner could have provided a calibration point for the study,

and helped to demonstrate definitively the repeatability of the

approach. This does not diminish the significance of the findings

of the research, but rather helps to identify topics to address with

future research. The findings have advanced knowledge by high-

lighting important considerations where MBES data are acquired

for image-based classification. In relation to the specific research

objectives outlined in the aims and objectives, the research has

(i) quantified the agreement between classifications obtained at

perpendicularly opposing orientations;

(ii) in the conditions tested, orientation has been demonstrated

to have had a more significant affect on the classification

than vessel speed;

(iii) identified, described, and demonstrated several principal

concerns for the acquisition of MBES data for image-based

classification.

AcknowledgementsThe survey was conducted in cooperation between the University

of Ulster and the Marine Institute, Ireland, as part of a Strategy for

Science Technology and Innovation Integrated Marine

Exploration Programme. The desk-based research was funded

through a PhD scholarship awarded by the Department of

Education and Learning, Northern Ireland. We thank the master

and crew of the RV Celtic Explorer, support staff Janine

Guinan (Marine Institute) for her role in data acquisition, and

the Marine Science Undergraduate students for their participation

in the research cruise in December 2008. We also thank Veerle

Huvenne and an anonymous reviewer for their helpful comments,which greatly improved the quality of the finished manuscript.

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