We present an application of the curvelet transform fusion method between geophysical and remote sensing data. The method was tested in two different archaeological areas in Greece with different historical, archaeological, and environmental characteristics. The final fused images combined all available information reducing the noise and enhancing the interesting features

Karamitrou, Alexandra

Tsokas, Gregory N.

English

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KIEL-UP • DOI: https://doi.org/10.38072/978-3-928794-83-1/p85 419 ADVANCES IN ON- AND OFFSHORE ARCHAEOLOGICAL PROSPECTION  Proceedings of the 15th International Conference on Archaeological Prospection  ICAP 2023Identification of buried archaeological features using the curvelet transform   Alexandra Karamitrou   1, 2*, Gregory N. Tsokas 11 Laboratory of Exploration Geophysics, Aristotle University of Thessaloniki, Greece2 Department of Archaeology, University of Southampton, United Kingdom * Corresponding author: E-mail: karamitrou@geophysics.grAbstractWe present an application of the curvelet transform fusion method between geophysical and remote sensing data. The method was tested in two different archaeological areas in Greece with different historical, archaeological, and environ-mental characteristics. The final fused images combined all available information reducing the noise and enhancing the interesting features.Keywordsarchaeology; curvelet transform; fusion; geophysics; remote sensing IntroductionOver the last decades geophysical methods have become a crucial part of the archaeological field exploration. All in-volved parts have recognized the importance of employing overlapping geophysical measurements with different met-hods as they deliver sensitivity to dissimilar subsurface pro-perties, allowing a more robust and complete representation of the targets (Ernenwein and Hargrave 2007; Gaffney 2008). One common combination is the electrical resistivity with the geomagnetic gradiometry method. This is mainly to in-tegrate the advantages of the resistivity in mapping targets such as building footings and other structural remains, with the sensitivity of the geomagnetic method to targets with substantial magnetic susceptibility e.g., ferrous metals, ce-ramic objects, and hearths (Kvamme et al. 2018). Additio-nally, remote sensing data have nowadays became a useful tool in archaeological research complimenting with data that provide easy access to even remote areas. Towards this direction, image fusion and in particular the curvelet-based fusion method (Karamitrou et al. 2019) allows the automa-tic integration of multiple overlapping datasets from various remote sensing methods, (e.g. Karamitrou et al. 2021) that delineate surface signatures of subsurface structures. In this work we demonstrate the advantages of this approach using data from two archaeological areas in Greece.MethodOnce the remote sensing images are acquired some basic processing is applied (e.g. arctan function, interpolation, dynamic range compression, high-pass filter, Wallis filter). While the data cover that same area of examination an additional fine co-registration is applied that further ad-justs fine-scale discrepancies associated with inaccuracies mainly since the geophysical measurements are taken using hand-held devices as well as from errors in the rela-tive positioning of the grids (Karamitrou et al. 2017). The next step is to apply the curvelet transform (CT) method where each image is decomposed (Candès and Donoho 2000) into discrete coefficients. These coefficients are functions of the spatial coordinates, scale (i.e. size), and azimuth of the imaged features. The CT is fully reversable and sparse. The main advantage of curvelet transform is that objects can effectively be represented as a series of smaller linear segments. The ability of the CT to distingu-ish the importance of imaged features according to their location, size, and orientation provides the means for an objective integration of different remote sensing images. The final fused image is created directly in the curvelet domain by comparing the corresponding CT coefficients of the initial input images and choosing the one with the maximum amplitude (“the maximum frequency rule”). ADVANCES IN ON- AND OFFSHORE ARCHAEOLOGICAL PROSPECTION  Proceedings of the 15th International Conference on Archaeological Prospection  ICAP 2023420  KIEL-UP • DOI: https://doi.org/10.38072/978-3-928794-83-1/p85DataWe applied the curvelet transform in two archaeological areas in Greece, the archaeological area of Philippi, located in Central Macedonia Region and the archaeological area of Stryme in Thrace. Each archaeological area represents a different era with different environmental and historical conditions. For the archaeological area of Philippi, we ex-amined two geophysical methods (magnetic gradiometry and the electrical resistance) and one aerial image taken using a drone. On the other hand, for the archaeological area of Stryme, we have examined an image depicting the electrical resistance of the area and a GeoEye-1 panchro-matic image with a resolution of 0.45 m.ResultsIn both cases the final fused images represent the exami-ned area in more details by enhancing the interesting features and suppressing the noise. From historical infor-mation and from excavations in the nearby locations of both areas of investigation we knew the orientation of the expected features. Therefore, during the fusion process we enhanced the images along these specific angles. Figure 1 shows an example from the archaeological area of Phillipi. A visual inspection reveals that at the N-NE region where there are fine scale structures the resistivity image contributes more while at the SW region where longer wavelength features exist the magnetic image prevail. A further statistical analysis of the final fussed images all-ows the evaluation in more details. This analysis can be done efficiently on-the-fly during the fusion by keeping track of the number of non-zero coefficients that each image contributes to the fused product across different levels of the decomposition (i.e. scales) and angles (i.e. azimuths) (Fig. 2). This analysis reveals that the contri-bution of each image varies significantly over different scales and different orientations.ConclusionImage fusion is a powerful tool that enables the visualiza-tion of all available overlapping imagery into one single image. Archaeological images include complex features of various sizes and shapes and curvelet transform has proven to be a reliable method that manage to process these features as it can elevates a 2-dimensional image into a 4-dimensional space where instead of the two spa-tial coordinates the image can be expressed as a function of space, scale and orientation. In the final fused images, the noise is suppressed while the interesting features ap-pear enhanced and, in many cases, more complete. Fig. 1: Geophysical images from the area of Phillipi in N. Greece a) Magnetic gradiometry, b) electrical resistance and c) final fused image using the curvelet method.ADVANCES IN ON- AND OFFSHORE ARCHAEOLOGICAL PROSPECTION  Proceedings of the 15th International Conference on Archaeological Prospection  ICAP 2023KIEL-UP • DOI: https://doi.org/10.38072/978-3-928794-83-1/p85 421 Acknowledgments This work has been accomplished on behalf of the pro-ject EKATY: Innovative imaging of the subsurface of ar-chaeological sites and the interior of structural elements of monuments in 3 and 4 Dimensions. The project is run-ning under the framework of the Operational Program-me Competitiveness, Entrepreneurship and Innovation 2014-2020 (EPAnEK), Special Actions “Aquaculture” – “Industrial Materials” – “Open Innovation in Culture”, Τ6ΥΒΠ-00211.  ReferencesCandès EJ, Donoho DL. Curvelets–a surprisingly effective non adap-tive representation for objects with edges. In Cohen A, Rabut C, Schumaker L. Curves and surface fitting. Proceedings of the 4th International Conference on Curves and Surfaces, Saint-Malo, France, 1-7 July 1999, Volume 2. Nashville, TN: Vanderbilt Uni-versity Press; 2000. p.105–120. Available from: https://apps.dtic.mil/sti/pdfs/ADP011978.pdfErnenwein EG, Hargrave ML. Archaeological Geophysics for DoD Field Use: a Guide for New and Novice Users. Washington DC: Environmental Security Technology Certification Program; 2007 Report for Project 200611. Available from: https://apps.dtic.mil/sti/citations/ADA596339Fig. 2: Comparison of the contribution of the magnetic (blue) versus the resistivity (red) image to the fused product, along different levels and angles (azimuths). Vertical axes count the number of coefficients each image contributed. Top row shows the approxima-tions, where no angular information is available.ADVANCES IN ON- AND OFFSHORE ARCHAEOLOGICAL PROSPECTION  Proceedings of the 15th International Conference on Archaeological Prospection  ICAP 2023422  KIEL-UP • DOI: https://doi.org/10.38072/978-3-928794-83-1/p85Gaffney C. Detecting trends in the prediction of the buried past: a review of geophysical techniques in Archaeology. Archaeometry 2008; 50:313-336.Karamitrou A, Bogiatzis P, Tsokas GN. Fusion between Geophysical and Satellite images for the study of Archaeological sites. Ar-chaeological Prospection 2020; 27:118-133. doi: 10.1002/arp.1766Karamitrou A, Tsokas NG, Kaimaris D, Dadaki S, Stampolidis A, Var-gemezis G, Tsourlos P, Fikos E. Joint Interpretation of Various Geophysical Data by Means of Image Fusion in Philippi in Nor-thern Greece. ArcheoSciences - revue d’archéométrie 202; 45: 259-262. doi: 10.4000/archeosciences.9954Karamitrou A, Tsokas GN, Petrou M. A Pixel-Based Semi-Stochastic Algorithm for the Registration of Geophysical Images. Archaeo-logical Prospection, 2017; 24:413-424. doi: 10.1002/arp.1578Kvamme KL, Ernenwein EG, Menzer JG. Putting all together: Geo-physical data integration. Persico R, Piro S, Linford N, editors. In-novation in Near-Ssurface Geophysics. instrumentation, applica-tion, and Data Processing Methods. Amsterdam: Elsevier; 2018. p. 287-339. doi: 10.1016/B978-0-12-812429-1.00009-X  Open AccessThis paper is published under the Creative Commons Attri-bution 4.0 International license (https://creativecommons.org/licenses/by/4.0/deed.en). Please note that individual, appropriately marked parts of the paper may be excluded from the license mentioned or may be subject to other co-pyright conditions. If such third party material is not under the Creative Commons license, any copying, editing or pu-blic reproduction is only permitted with the prior consent of the respective copyright owner or on the basis of rele-vant legal authorization regulations.

Identification of buried archaeological features using the curvelet transform

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Identification of buried archaeological features using the curvelet transform

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