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Working with Gaussian Random Noise for Multi-Sensor Archaeological Prospection: Fusion of Ground Penetrating Radar Depth Slices and Ground Spectral Signatures from 0.00 m to 0.60 m below Ground Surface

1
Eratosthenes Research Centre, Department of Civil Engineering and Geomatics, Faculty of Engineering and Technology, Cyprus University of Technology, Saripolou 2-8, Limassol 3036, Cyprus
2
The Archaeological Research Unit, Department of History and Archaeology, University of Cyprus, P.O. Box. 20537, Nicosia 1678, Cyprus
3
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.
Remote Sens. 2019, 11(16), 1895; https://doi.org/10.3390/rs11161895
Received: 25 July 2019 / Revised: 9 August 2019 / Accepted: 9 August 2019 / Published: 13 August 2019
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Abstract

The integration of different remote sensing datasets acquired from optical and radar sensors can improve the overall performance and detection rate for mapping sub-surface archaeological remains. However, data fusion remains a challenge for archaeological prospection studies, since remotely sensed sensors have different instrument principles, operating in different wavelengths. Recent studies have demonstrated that some fusion modelling can be achieved under ideal measurement conditions (e.g., simultaneously measurements in no hazy days) using advance regression models, like those of the nonlinear Bayesian Neural Networks. This paper aims to go a step further and investigate the impact of noise in regression models, between datasets obtained from ground-penetrating radar (GPR) and portable field spectroradiometers. Initially, the GPR measurements provided three depth slices of 20 cm thickness, starting from 0.00 m up to 0.60 m below the ground surface while ground spectral signatures acquired from the spectroradiometer were processed to calculate 13 multispectral and 53 hyperspectral indices. Then, various levels of Gaussian random noise ranging from 0.1 to 0.5 of a normal distribution, with mean 0 and variance 1, were added at both GPR and spectral signatures datasets. Afterward, Bayesian Neural Network regression fitting was applied between the radar (GPR) versus the optical (spectral signatures) datasets. Different regression model strategies were implemented and presented in the paper. The overall results show that fusion with a noise level of up to 0.2 of the normal distribution does not dramatically drop the regression model between the radar and optical datasets (compared to the non-noisy data). Finally, anomalies appearing as strong reflectors in the GPR measurements, continue to provide an obvious contrast even with noisy regression modelling. View Full-Text
Keywords: fusion; archaeological prospection; buried archaeological remains; ground penetrating radar (GPR); ground spectral signatures; Bayesian Neural Network fusion; archaeological prospection; buried archaeological remains; ground penetrating radar (GPR); ground spectral signatures; Bayesian Neural Network
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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. Working with Gaussian Random Noise for Multi-Sensor Archaeological Prospection: Fusion of Ground Penetrating Radar Depth Slices and Ground Spectral Signatures from 0.00 m to 0.60 m below Ground Surface. Remote Sens. 2019, 11, 1895.

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