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.

Highlights

  • The detection of sub-surface buried targets remains a challenge for different disciplines such as archaeological prospection [1,2,3]; forensic archaeology [4,5,6]; urban planning [7,8,9]; military and security purposes [10,11]

  • Detection of archaeological remains is usually achieved based on the exploitation of remote sensing sensors

  • The use of multi-source datasets is quite familiar in the area of archaeological prospection offering new technologies for the detection of sub-surface archaeological remains [15,16,17,18]

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Summary

Introduction

The detection of sub-surface buried targets remains a challenge for different disciplines such as archaeological prospection [1,2,3]; forensic archaeology [4,5,6]; urban planning (e.g., pipe detection) [7,8,9]; military and security purposes (e.g., land-mines) [10,11].Detection of archaeological remains is usually achieved based on the exploitation of remote sensing sensors. The detection of sub-surface buried targets remains a challenge for different disciplines such as archaeological prospection [1,2,3]; forensic archaeology [4,5,6]; urban planning (e.g., pipe detection) [7,8,9]; military and security purposes (e.g., land-mines) [10,11]. As a non-destructive tool, remote sensing technology offers several advantages to researchers, including fast prospecting and mapping, acquisition of digital geospatial information, and a multi-scale coverage from a local to a regional level. The use of multi-source datasets is quite familiar in the area of archaeological prospection offering new technologies for the detection of sub-surface archaeological remains [15,16,17,18]. The current integration of the different prospection results as those obtained from spaceborne, airborne and ground sensors is analysed in a Geographical Information System (GIS) environment through the superposition of the various thematic layers [19,20,21]

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