Signal Processing for Underwater Archaeology

Davide Moroni, Maria Antonietta Pascali, Marco Reggiannini and Ovidio Salvetti

SILab, Institute of Information Science and Technololgies - CNR, Via G. Moruzzi 1, 56124, Pisa, Italy

Keywords: Underwater Robotics, Underwater Cultural Heritage, Image-based Modelling and 3D Reconstruction,

Multi-Sensor Data Analysis, Archaeological Object Recognition.

Abstract: About three million wrecks lie scattered on the oceans’ seafloors. This huge patrimony is actually

threatened by criminal enterprises having advanced tools available for localization and rescue operations.

ARROWS, a currently ongoing EU FP7 project, is an example of the effective commitment between

cultural institutions and the scientific community towards the safeguard of the sunken cultural heritage.

ARROWS is devoted to advanced technologies and tools for mapping, diagnosing, cleaning, and securing

underwater and coastal archaeological sites. A fleet of Autonomous Underwater Vehicles (AUVs) will be

manufactured with the purpose of surveying the seabed and sensing the underwater environment by means

of proper payload sensors (digital cameras, side scan and multi-beam sonars). This paper describes a set of

underwater scene understanding procedures specifically tailored to the purposes addressed in the ARROWS

frame. In particular the data collected by the AUVs during the acquisition campaigns will be processed to

detect targets of interest located on the seabed. The main approach adopted in the object detection

procedures is to highlight the amount of regularity in the captured data. This can be pursued by exploiting

computer vision algorithms that perform i) the recognition of geometrical curves ii) the classification of

seafloor areas by means of textural pattern analysis iii) a large scale map generation to return an overall

view of the site and iv) a reliable object recognition process performing the integration of the available multi

modal information. Moreover the collected raw data together with the analysis output results will be stored

to allow for an offline deep analysis of the archaeological findings. This will represent a powerful tool to be

used by expert users or by the general public to enjoy the underwater cultural heritage.

1 INTRODUCTION

The marine environment represents a hostile frame

for any kind of human or scientific activity. The

peculiar setting imposes strict constraints for survey,

mapping and rescue of sunken objects. Given the

huge number of wrecks scattered all over the globe’s

seabed, around three million according to UNESCO

reports (http://www.unesco.org/), joint actions

between cultural institutions and scientific

communities have been fostered by the European

Union in order to commit for the safeguard of the

sunken cultural heritage.

One of the joint venture aiming at the above cited

goals is ARROWS, an EU FP7 project devoted to

the development of low cost technologies for marine

archaeological site detection and preservation

operations. The ARROWS researchers, guided by

the suggestions of a dedicated Archaeological

Advisory Group (AAG), look for solutions to the

mapping, diagnosis and cleaning tasks of underwater

archaeological sites. To those particular aims a team

of new heterogeneous Autonomous Underwater

Vehicles (AUVs) is currently being developed.

Those AUVs are designed to host optical (digital

cameras) and/or acoustic (Sidescan sonar,

Multibeam echo-sounder) payload sensors in order

to endow the system with environment sensing

capabilities. The captured data will be fed to a scene

understanding procedure performing manmade

object detection tasks. To this purpose the adopted

criteria is to highlight the regularity content in the

data. In this framework we consider regular those

areas containing parts of primitive curves, like lines,

circles and ellipses.

Regularity can also be assessed by analysing the

objects surface appearance in terms of the repeating

textural patterns detected in the captured maps.

Based on those features, we can perform attentive

analysis of the environment by giving to an area a

label of interest proportional to the regularity

content: more regular areas are marked with higher

ranks while chaotic and unstructured area will be

marked with low ranks.

Moroni D., Pascali M., Reggiannini M. and Salvetti O..

Signal Processing for Underwater Archaeology.

DOI: 10.5220/0005462100800084

In Proceedings of the 5th International Workshop on Image Mining. Theory and Applications (IMTA-5-2015), pages 80-84

ISBN: 978-989-758-094-9

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

In the first part of this paper the main features of the

algorithms implementing the detection and

classification tasks will be described. The remaining

part of the paper describes the major activity

emerging after the project experimental campaigns,

concerning the postprocessing of the collected data

and the generation of two primary results: i) the 3D

models of the detected archaeological objects and ii)

a set of large scale maps containing the result of an

image mosaicking process, providing an overall

view of the surveyed area. These output results are

mainly oriented to directly involved cultural

operators, enabling them to study in detail every

single object without moving it from its

environmental framework, but will be also available

for dissemination and fruition of the underwater

cultural heritage by the general public.

2 AUV SENSING SYSTEM

The main goal of the ARROWS missions is to

perform a systematic mapping of the marine

seafloors and to process the output maps to detect

and classify potential archaeological targets. To that

aim the underwater vehicles outlined in the previous

section will be equipped with a proper set of sensor

devices, e.g. optical cameras and acoustic sonars.

These sensors represent appealing choices to the

oceanographic engineer since they provide

complementary information about the surrounding

environment. Generally speaking acoustic sensors

are exploited to create large scale maps of the

environment while cameras provide more detailed

images of the targets.

The AUV payload equipment will consist of a

couple of digital cameras plus an acoustic device

optionally selected between a sidescan sonar or a

multibeam echosounder, either forward looking or

bathymetric.

3 PAYLOAD DATA PROCESSING

Since the chosen sensor typologies operate on

different principles the captured data are affected by

different distortions, relating to both systematic as

well as environmental sources of corruption. The

cameras introduce geometrical distortions in the

images because of the propagation of

electromagnetic waves through the optical unit.

Moreover the optical signal is affected by strong

degradation due to energy absorption in the water

medium.

On the other hand acoustic sonars are affected as

well by geometrical distortions. That is due to the

peculiar perception of the environment: e.g. side

scan sonar maps contain a central black stripe which

is generated by the propagation of acoustic waves

through the water column. That represents useless

information that has to be erased in order to restore

the correct geometrical properties of the data.

Intensive fluctuations in the pose of the vehicle

which is hosting the sensors, may represent a

relevant source of geometry distortion of the data. In

case of strong oscillations of the vehicle induced by

intense waves or currents this can represent a

dominant issue. This issue highlights the strong need

for the synchronization of the optical and acoustic

data with the navigation data records, in order to get

a proper correction. Under the hypothesis that the

whole set of noise sources can be reduced by proper

restoration and geometry correction techniques the

successive goal is to analyse the output data to

provide an informative description of the

environment.

3.1 Geometry Assessment

The assessment of primitive curves segments in an

image is a typical computer vision issue that has

been tackled in many ways. In order to fulfil the

curve detection purposes within the ARROWS

project, a dedicated procedure has been developed.

The implemented algorithm is based on a statistical

approach in order to provide the system with enough

reliability and computational performances. The

application of the algorithm, based on the Gestalt

theory (Patraucean, 2012), is more thoroughly

described in (Moroni, 2013; Moroni, 2014). Some

results are showed in Figure 1.

Figure 1: Application of the curve detection algorithm to a

side scan sonar image detail (image taken from

http://www.jwfishers.com/).

3.2 Texture Analysis

Texture is a descriptor of the surface appearance of

objects. This parameter can be exploited to discern

between different kinds of objects and to assign each

SignalProcessingforUnderwaterArchaeology

of them to a specific class. In the special case of

underwater mapping, textural analysis is employed

to classify the surveyed environment into seafloor

categories (sand, rock, vegetation). This enables the

detection of anomalies that can be related to

potentially interesting objects.

Within the many descriptors available in the

literature we chose a method based on the Gabor

filters (Jain, 1991). Mathematically speaking a

Gabor filter is a 2D sinusoid, with specific

orientation and frequency values, modulated by a

Gaussian function. The convolution of this filter

with an image results in a map where the regions

exhibiting frequency and orientation values similar

to the filter ones are emphasized. By varying

frequency and orientation and repeating the

convolution operation a set of filter responses is

obtained. Those responses can be clustered

according to the dominant components. This way

every pixel in the image will be assigned to a

specific class. The application of Gabor filters for

textural analysis purposes is illustrated in Figure 2.

Figure 2: Side scan sonar image segmentation by

exploiting of a Gabor filtering technique (image taken

from http://www.ise.bc.ca/).

3.3 Seabed Bathymetry

In the ocean’s setting the multibeam echosounder is

typically exploited for detailed reconstruction of

limited areas of the seafloor. The sensor technology

is based on the simultaneous collection of echoes

from the scattering points located on the seafloor

acoustic footprint. During July 2014 a dedicated

experimental session has been carried out at the

Ocean Systems Lab, Heriot Watt University of

Edinburgh. In that circumstance a BlueView MBES

2250 has been employed to survey a small pool

facility available in the lab.

The raw output data returned by the MB 2250

consist of 2D maps representing the echo returns

collected along the different directions of the

acoustic fan (Figure 3). These maps require a pre-

processing stage to emphasize and extract the

relevant signal from a complex and unstructured

background. To this aim the raw acoustic data has

Figure 3: Example of a multibeam echosounder map.

Figure 4: 3D mesh resulting from the processing of the

multibeam echosounder maps.

been pre-filtered using a median mask to reduce the

corruption associated with the granular noise

(Blondel, 2009) affecting the signal.

Later the signal has been manipulated to isolate the

connected structure corresponding to the first

echoes. To this aim a cascade of morphological

transformations (Gonzalez, 2008) have been applied

to the map to i) identify the connected components

in the map as those structures exhibiting continuity

features and ii) to smooth the contour of the objects

and to discard undesired protrusions by means of an

opening operator. Finally the bathymetry has been

estimated integrating the pre-processed map and the

knowledge of the vehicle position and attitude

obtained from auxiliary sensors measuring the

vehicle pose. By repeating this operation for every

captured map it has been possible to reconstruct the

3D morphology of the pool facility as a set of x, y, z

points. The point cloud can be finally refined by

generating a mesh (Figure 4).

3.4 Large Scale Maps

In the underwater setting the maximum observable

area is inevitably reduced due to the sensor limited

performances and to the unfavourable environmental

IMTA-52015-5thInternationalWorkshoponImageMining.TheoryandApplications

conditions. By exploiting a mosaicking technique

(Elibol, 2014; Prados, 2014) the data can be

represented in an overall coherent framework,

showing clearly the spatial relationship between

structures on the seafloor, even if captured at non-

consecutive instants.

Figure 5: Image mosaic of the small pool at the Ocean

Systems Lab obtained by registering and stitching the

captured optical data.

SIFT correspondences can be identified in the

overlapping areas of chronologically consecutive

maps and exploited to refer all the captured data to a

common coordinate system (Lowe, 2004). The

following step is to fuse all the data in an overall

single synthesis map, consisting of the output

mosaic map. An example of mosaicking technique

applied to the data captured at the Ocean Systems

Lab pool is represented in Figure 5.

3.5 Data Integration

As stated in the previous sections, each sensor

employed in the survey missions will provide an

individual description of the environment. As far as

a robust object recognition process is pursued, it is

interesting to conceive a synthesis structure

summarizing all the informative content related to an

area of the seabed. This can be formally expressed

by introducing a multi-dimensional map, made up of

multiple layers. A point in this map gives details

about the whole information available for the

corresponding point in the world. This refers to

information concerning (i) the raw captured data, (ii)

the results of data analysis algorithms and (iii) the

bathymetry collected by proper sensors or estimated

by computer vision procedures. It is expected that

considering the whole set of available information

can be an efficient way to perform robust object

recognition, reliable with respect to false alarms

rejection. An example of data integration result,

obtained by stitching the camera images mosaic on

the multi-beam bathymetry is illustrated in Figure 6.

Figure 6: Bathymetric map, obtained by integrating a

multi-beam echo-sounder map and an optical mosaic. The

data have been captured during an experiment performed

in the small pool facility of the Ocean Systems Lab, Heriot

Watt University, Edinburgh.

4 CONCLUSIONS

The overall aim of the project is to provide the

archaeologist with tools to be exploited in multiple

ways. First of all the robotic and automation

technology presented in this paper will make easier

the archaeologist procedures, carried out in the

typical hostile and complex underwater

environment. As a byproduct, the archaeologist will

be provided with techniques to perform indirect

measurements and to formulate an historical

interpretation on the findings. Finally, in order to

disseminate knowledge regarding the underwater

cultural heritage and to increase the sensitivity to the

problem of its preservation, the developed tools will

be addressed different audiences, including the

general public. In particular, one of the purposes of

the project is to devise new dissemination channels

making use of 3D immersive representations. The

resulting simulated environment will be populated

by the models generated during the project post-

processing stage. In the following months, the

developed methodology will be tested by organizing

specific campaigns in two European sites, one in

Italy, in the Egadi Archipelagos, and one in the

Baltic Sea. All the collected data will be processed

using the methods reported in this paper and will be

used for assessing the validity of our approach. As a

result, a set of 3D scenes will be produced, with the

aim of replicating the experience of wreck

exploration and survey.

SignalProcessingforUnderwaterArchaeology

ACKNOWLEDGEMENTS

The activity described in this paper has been

supported by the ARROWS project. The project has

received funding from the European Union’s

Seventh Framework Programme for research,

technological development and demonstration under

grant Agreement no. 308724.

The authors would like to thank Dr. Pamela

Gambogi, Executive Archaeologist and Coordinator

of the Underwater Operational Team (N.O.S) of the

Tuscany Archaeological Superintendence.

The authors would also like to thank Professor Yvan

Petillot and Professor David Lane from Heriot Watt

University of Edinburgh, for kindly providing access

to the facilities of the Ocean Systems Laboratory.

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IMTA-52015-5thInternationalWorkshoponImageMining.TheoryandApplications