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Remote Sens. 2018, 10(11), 1762; https://doi.org/10.3390/rs10111762

Beyond GIS Layering: Challenging the (Re)use and Fusion of Archaeological Prospection Data Based on Bayesian Neural Networks (BNN)

1
Department of Civil Engineering and Geomatics, Eratosthenes Research Center, Cyprus University of Technology, Saripolou 2-8, Limassol 3036, Cyprus
2
Laboratory of Geophysical-Satellite Remote Sensing and Archaeo-Environment, Foundation for Research and Technology, Hellas (F.O.R.T.H.), 74100 Rethymno, Greece
*
Author to whom correspondence should be addressed.
Received: 2 September 2018 / Revised: 26 October 2018 / Accepted: 6 November 2018 / Published: 8 November 2018
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Abstract

Multisource remote sensing data acquisition has been increased in the last years due to technological improvements and decreased acquisition cost of remotely sensed data and products. This study attempts to fuse different types of prospection data acquired from dissimilar remote sensors and explores new ways of interpreting remote sensing data obtained from archaeological sites. Combination and fusion of complementary sensory data does not only increase the detection accuracy but it also increases the overall performance in respect to recall and precision. Moving beyond the discussion and concerns related to fusion and integration of multisource prospection data, this study argues their potential (re)use based on Bayesian Neural Network (BNN) fusion models. The archaeological site of Vésztő-Mágor Tell in the eastern part of Hungary was selected as a case study, since ground penetrating radar (GPR) and ground spectral signatures have been collected in the past. GPR 20 cm depth slices results were correlated with spectroradiometric datasets based on neural network models. The results showed that the BNN models provide a global correlation coefficient of up to 73%—between the GPR and the spectroradiometric data—for all depth slices. This could eventually lead to the potential re-use of archived geo-prospection datasets with optical earth observation datasets. A discussion regarding the potential limitations and challenges of this approach is also included in the paper. View Full-Text
Keywords: remote sensing archaeology; fusion; neural networks; re-use; GPR; spectral signatures; Hungary remote sensing archaeology; fusion; neural networks; re-use; GPR; spectral signatures; Hungary
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Agapiou, A.; Sarris, A. Beyond GIS Layering: Challenging the (Re)use and Fusion of Archaeological Prospection Data Based on Bayesian Neural Networks (BNN). Remote Sens. 2018, 10, 1762.

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