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4. Processing and visualisation of data

Joint Interpretation of Various Geophysical Data by Means of Image Fusion in Philippi in Northern Greece

Alexandra Karamitrou, Gregory N. Tsokas, Dimitrios Kaimaris, Stavroula Dadaki, Alexandros Stampolidis, George Vargemezis, Panagiotis Tsourlos et Elias Fikos
p. 259-262

Résumé

– Resistance and magnetic mapping, ERTs and aerial photography put together.

– Fusion was attempted through curvelet transform.

– The useful information of all methods is given in a combined image at the site of Philippi in Greece.

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Entrées d’index

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Texte intégral

This work was accomplished on behalf of “EKATY: Innovative imaging of the subsurface of archaeological sites and the interior of structural elements of monuments in 3 and 4 dimensions”. The project runs within the framework of the Operational Programme Competitiveness, Entrepreneurship and Innovation 2014-2020 (EPAnEK), Special Actions “Aquaculture” – “Industrial Materials” – “Open Innovation in Culture”, Τ6ΥΒΠ-00211.

“I will see thee at Philippi, then…”
Shakespeare: Julius Cesar; act 4; scene 3

Introduction

1Ever since the pioneering work of R. Atkinson and M. Aitken (Atkinson, 1953), archaeological prospection has continued to develop and has become an invaluable tool of archaeological research (Ernenwein & Hargrave, 2007; Gaffney, 2008; Tsokas et al., 2009; Tsokas et al., 2012; Schmidt et al., 2020). With developments in instrumentation and hardware, as well as algorithms and software, new paths have opened before the discipline, fostering the application of arrays of geophysical methods, usually combined with aerial photographs, digital terrain models (DTM), Lidar data, etc. (e.g., Piro et al., 2000; Kvamme, 2006, 2007; Sarris et al., 2013; Karamitrou et al., 2017; 2020; Kvamme et al., 2019; Li et al., 2019). The growing amount of information on the subsurface setting provided by mapping the distribution of various physical parameters has prompted the development of algorithms combining and interpreting jointly the outcomes of various methods. This supports the clearly advantageous process of extracting information provided by individual methods and presenting it alongside data obtained with the application of other methods.

2The paper presents the results of the application of a multi-resolution image fusion scheme based upon the curvelet transform (Candes & Donoho, 2000), introduced in archaeology by Karamitrou et al. (2020), to data coming in this particular case from the exploration of the archaeological site of Philippi in northern Greece (region of Eastern Macedonia and Thrace).

The site

3The ancient city was founded as a small town called Krenides (fountains in Greek) in the mid-4th century BC, but quickly grew in importance after being conquered by Philip II of Macedon, who renamed it Philippi after himself. It was a stronghold strategically located on the great royal route running east-west across ancient Macedonia, which would later comprise part of the Roman Via Egnatia leading from Dyrrachium to Byzantium (later Constantinople. It flourished in Hellenistic and Roman times, also as an agricultural centre thanks to the draining of the marshes west of the town and the discovery of gold sources nearby. The city is known also as the site of the final battle between the successors of Julius Caesar, Mark Antony and Octavian, and the forces of his assassins led by Brutus and Cassius. Later, it was also the first place in Europe visited by the Apostle Paul (AD 49-50) and the place where the first Europeans were converted to Christianity and the first European Christian church was established. Roman emperors –Claudius, Antoninus Pius and Marcus Aurelius– adorned the city with magnificent public buildings. The city was abandoned in the 14th century during the Ottoman advance into Europe. Nowadays, it has been declared as a World Heritage Site by Unesco.

Data

4An extended geophysical prospection program implemented at the site consists of electrical resistance and magnetic field mapping. Infrared and panchromatic aerial photographs have been taken, both of the ancient intra muros urban complex and the extra muros Philippi plain. Electrical Resistivity Tomography (ERT) data, acquired in specific locations, complement the other data sets and have clarified specific issues raised by the mapped data.

5Electrical resistance mapping has employed classical techniques along profiles spaced 1-m apart, stepwise at 1-m intervals. The magnetic field vertical gradient was measured applying an array of four Ferex fluxgate gradiometers, fixed at 0.5 m intervals. The in-line spacing was very dense with one reading every 0.05 m.

6ERTs were conducted employing both the dipole-dipole and the multi-gradient array (Dahlin & Zhou, 2006) in order to benefit from the different merits of each. The data acquisition performed in grids of parallel and equidistant profiles. In this way, though data collection was not full 3-D, inversion was performed employing 3D algorithms. This approach has been shown to generate minimum information loss relative to fully 3D surveying (Papadopoulos et al., 2006). Thus, the ERTs were subjected into the 3D processing scheme proposed by Tsourlos and Ogilvy (1999).

Method

7Images yielded by the different methods described above are registered first in order to correct for arbitrary local dislocations that commonly exist in the data due to various reasons, including physical obstacles, micro-topography and measurements taken with handheld devices. A semi-stochastic approach is used, based on the measurable mutual information of the tested images, as described in Karamitrou et al. (2017).

8The images are then combined (fused) using the technique presented in Karamitrou et al. (2020). More specifically, the images are initially decomposed using the curvelet transform (Candes & Donoho, 2000). Mapping the images into the curvelet domain allows the data (e.g., variations in electrical resistance, magnetic field vertical gradient) to be represented in four dimensions: as a function of the scale of the features and their spatial orientation in addition to their two spatial Cartesian coordinates. The fused image is synthesized in the curvelet domain by selecting the desired features in each dimension among the initial images. Finally, the inverse curvelet transform yields the fused image in Cartesian/pixel coordinates. This approach is presented also in another related paper in this volume (Karamitrou & Tsokas, 2021).

Discussion and conclusions

9Different geophysical methods applied at the site of Philippi provided insight for the distribution of different physical parameters like subsurface electrical resistance and magnetic properties, which reflect the underground vestiges of past human activity. In a typical approach, the dissimilar geophysical data sets were visualized as raster images providing a map-view image of the subsurface archaeological features.

10Images from each method were initially considered separately, e.g. the distribution of resistance and vertical magnetic gradient in a plot close to the eastern wall of the ancient city (Fig. 1). This particular example shows linear anomalies articulated together, clearly reflecting buried ancient architecture in this part of the city.

Figure 1. Left: resistance mapping; right: magnetic image of part of the archaeological site of Philippi in northern Greece.

Figure 1. Left: resistance mapping; right: magnetic image of part of the archaeological site of Philippi in northern Greece.

11Next, the images were jointly analyzed by means of the curvelet-based image-fusion method in order to extract useful information from each and include it into the combined and fused images. In addition to geophysical images, combinations of geophysical images and aerial photographs were also produced.

12The results suggest that the information content of the fused images is superior to that offered separately by each method (Fig. 2). The results revealed several public buildings, dwelling complexes and other urban components of the ancient city. Among the benefits of the fused image is that it combines archaeological features observed separately in the initial images from the different methods due to different geophysical properties producing, for instance, an electrical resistance anomaly but not a magnetic one, and vice versa. For example, the integration of linear targets can be observed in the fused image (Fig. 2). Furthermore, the image also integrates different parts of the same features that are only partly visible in the initial images (e.g., bottom left part of Fig. 2), allowing a more holistic interpretation of the site and tighter assessment of the robustness of certain features. These properties of the fused images demonstrate the merits of employing image fusion methods to the results of integrated surveys, that is, wherever more than one method has been applied.

13Of course, image fusion (or integration of results, or simply the art of putting all together) has a long experimental and research route ahead of it, including addressing issues such as the optimal ways of combining features, especially in the case of multimodal data, effective separation or rejection of noise from archaeological features during the fusion process and best practices for visualization of the results. However, it is a very promising and highly rewarding field.

Figure 2. Fused image using the curvelet transform between the electrical resistivity and the magnetic image.

Figure 2. Fused image using the curvelet transform between the electrical resistivity and the magnetic image.
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Bibliographie

Atkinson, R.J.C., 1953. Field Archaeology. Methuen, London, 2nd edition.

Candes, E.J., Donoho, D.L., 2000. Curvelets – a surprisingly effective non adaptive representation for objects with edges. In A. Cohen, C. Rabut & L. Schumaker (eds.), Curves and surface fitting: Saint-Malo 1999, Vanderbilt University Press, Nashville, 105-120.

Dahlin, T., Zhou, B., 2006. Multiple-gradient array measurements for multichannel 2D resistivity imaging. Near Surface Geophysics, 4: 113-124.

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Table des illustrations

Titre Figure 1. Left: resistance mapping; right: magnetic image of part of the archaeological site of Philippi in northern Greece.
URL http://journals.openedition.org/archeosciences/docannexe/image/9954/img-1.jpg
Fichier image/jpeg, 2,3M
Titre Figure 2. Fused image using the curvelet transform between the electrical resistivity and the magnetic image.
URL http://journals.openedition.org/archeosciences/docannexe/image/9954/img-2.jpg
Fichier image/jpeg, 2,6M
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Référence papier

Alexandra Karamitrou, Gregory N. Tsokas, Dimitrios Kaimaris, Stavroula Dadaki, Alexandros Stampolidis, George Vargemezis, Panagiotis Tsourlos et Elias Fikos, « Joint Interpretation of Various Geophysical Data by Means of Image Fusion in Philippi in Northern Greece »ArcheoSciences, 45-1 | 2021, 259-262.

Référence électronique

Alexandra Karamitrou, Gregory N. Tsokas, Dimitrios Kaimaris, Stavroula Dadaki, Alexandros Stampolidis, George Vargemezis, Panagiotis Tsourlos et Elias Fikos, « Joint Interpretation of Various Geophysical Data by Means of Image Fusion in Philippi in Northern Greece »ArcheoSciences [En ligne], 45-1 | 2021, mis en ligne le 16 août 2021, consulté le 11 novembre 2025. URL : http://journals.openedition.org/archeosciences/9954 ; DOI : https://doi.org/10.4000/archeosciences.9954

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Auteurs

Alexandra Karamitrou

Laboratory of Exploration Geophysics, Aristotle University of Thessaloniki, School of Geology, 54124 Thessaloniki, Greece, http://geophysics.geo.auth.gr/ApplGeo/Laboratory of Exploration Geophysics, Aristotle University of Thessaloniki, School of Geology, 54124 Thessaloniki, Greece, http://geophysics.geo.auth.gr/ApplGeo/

Articles du même auteur

Gregory N. Tsokas

Laboratory of Exploration Geophysics, Aristotle University of Thessaloniki, School of Geology, 54124 Thessaloniki, Greece, http://geophysics.geo.auth.gr/ApplGeo/

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Dimitrios Kaimaris

School of Urban-Regional Planning and Development Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

Stavroula Dadaki

Ephorate of Antiquities of Kavala, Ministry of Culture, 14 Cyprus Str., 65110 Kavala

Alexandros Stampolidis

Laboratory of Exploration Geophysics, Aristotle University of Thessaloniki, School of Geology, 54124 Thessaloniki, Greece, http://geophysics.geo.auth.gr/ApplGeo/

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George Vargemezis

Laboratory of Exploration Geophysics, Aristotle University of Thessaloniki, School of Geology, 54124 Thessaloniki, Greece, http://geophysics.geo.auth.gr/ApplGeo/

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Panagiotis Tsourlos

Laboratory of Exploration Geophysics, Aristotle University of Thessaloniki, School of Geology, 54124 Thessaloniki, Greece, http://geophysics.geo.auth.gr/ApplGeo/

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Elias Fikos

Laboratory of Exploration Geophysics, Aristotle University of Thessaloniki, School of Geology, 54124 Thessaloniki, Greece, http://geophysics.geo.auth.gr/ApplGeo/

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