Fusion of geophysical images in the study of archaeological sites

Karamitrou, Alexandra; Bogiatzis, Petros; Τσοκασ, Γ. Ν.

doi:10.1002/arp.1766

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…The final step is to perform the inverse transformation and obtain the fused image. This fusion technique is described analytically in Karamitrou et al, 2019.…”

Section: Methodsmentioning

confidence: 99%

Curvelet-Transform-Based Fusion: Application to Europos and Doriskos, N. Greece

Karamitrou¹,

Tsokas²,

Stampolidis³

2021

archeosciences

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– We use a new approach to combine magnetic and electrical geophysical data from two different archaeological areas in Greece (Europos & Doriskos).– Using the curvelet transform we express the feature of the images as a function of spatial coordinates, scale, and orientation.– A combined image is created in the curvelet domain through the objective selection of the most salient features.– The results show several potential archaeological structures.

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“…The final step is to perform the inverse transformation and obtain the fused image. This fusion technique is described analytically in Karamitrou et al, 2019.…”

Section: Methodsmentioning

confidence: 99%

Curvelet-Transform-Based Fusion: Application to Europos and Doriskos, N. Greece

Karamitrou¹,

Tsokas²,

Stampolidis³

2021

archeosciences

Self Cite

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show abstract

“…The concept of data fusion was previously applied in geophysics using some of the approaches mentioned, highlighting the work developed by Hilbert et al (2012) and Karamitrou et al (2019).…”

Section: Data Fusion Conceptsmentioning

confidence: 99%

Geophysical data fusion of ground-penetrating radar and magnetic datasets using 2D wavelet transform and singular value decomposition

Oliveira¹,

Caldeira²,

Teixidó

et al. 2022

Front. Earth Sci.

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This work addresses the problem of the lack of perceptibility that geophysical data may have. Data fusion allows us to combine datasets, providing an improved and more informative source of information about structures buried in the ground. After testing different approaches, a strategy was developed using ground-penetrating radar and magnetic datasets collected over the same area. Data collected at the Roman Villa of Pisões (Beja, Portugal), which is a place of easy application of geophysical methods, were used to test the method, but with problems caused by the properties of the soil. The approach was based on processing operations that allow the fusion of images obtained by different equipment widely used in medical imaging for tumor detection and image processing. The goal is to create an improved image with data fusion that has higher quality than the input images, allowing a better understanding of the object of the study. The approach is composed of two stages: pre-processing and data fusion. Pre-processing is applied to enhance the input data. It consists of removing background noise through singular value decomposition applied in the spectral domain. Then the calculation of the data entropy will highlight the differences corresponding to the spatial alignments compatible with buried structures. Then, both entropy maps of the two datasets are fused in the second processing step to produce the final image. This step involves applying the 2D wavelet transform to each entropy map, decomposing them into sub-bands. Algorithms to calculate multiresolution singular value decomposition and the image gradient are applied to the sub-bands. The processed sub-band pairs are then fused using specific fusion rules. The fused image is obtained by applying the inverse of the wavelet transform. Data fusion with the proposed approach allows us to obtain a detailed image that is sharper and of better quality than the input datasets. The increase in sharpness and quality can be quantified through the sharpness index and the BRISQUE quality index in several steps of the processing. The obtained values confirm the graphical results. Images produced by the proposed data fusion approach suggest that the perceptibility has increased, allowing us to provide conclusions about the existence of buried structures.

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“…The results revealed the limitations and benefits of each method while showing the potential to improve data interpretation. Similar studies, where integration methods are compared, are conducted using synthetic data [32], real data measured by multiple geophysical methods from different sites [33], or both synthetic and real data [34]. Other attempts involved developing a methodology that integrates GPR data with multispectral satellite images to enhance the interpretability of detected archaeological targets in the near-surface layers [35,36].…”

Section: Data Fusionmentioning

confidence: 99%

GPR Data Interpretation Approaches in Archaeological Prospection

2021

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This article focuses on the possible drawbacks and pitfalls in the GPR data interpretation process commonly followed by most GPR practitioners in archaeological prospection. Standard processing techniques aim to remove some noise, enhance reflections of the subsurface. Next, one has to calculate the instantaneous envelope and produce C-scans which are 2D amplitude maps showing high reflectivity surfaces. These amplitude maps are mainly used for data interpretation and provide a good insight into the subsurface but cannot fully describe it. The main limitations are discussed while studies aiming to overcome them are reviewed. These studies involve integrated interpretation approaches using both B-scans and C-scans, attribute analysis, fusion approaches, and recent attempts to automatically interpret C-scans using Deep Learning (DL) algorithms. To contribute to the automatic interpretation of GPR data using DL, an application of Convolutional Neural Networks (CNNs) to classify GPR data is also presented and discussed.

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Fusion of geophysical images in the study of archaeological sites

Cited by 9 publications

References 26 publications

Curvelet-Transform-Based Fusion: Application to Europos and Doriskos, N. Greece

Curvelet-Transform-Based Fusion: Application to Europos and Doriskos, N. Greece

Geophysical data fusion of ground-penetrating radar and magnetic datasets using 2D wavelet transform and singular value decomposition

GPR Data Interpretation Approaches in Archaeological Prospection

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