Enhancement of Archaeological Proxies at Non-Homogenous Environments in Remotely Sensed Imagery

Athos Agapiou
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Athos Agapiou: Eratosthenes Research Centre, Department of Civil Engineering and Geomatics, Faculty of Engineering and Technology, Cyprus University of Technology, Saripolou 2-8, 3036 Limassol, Cyprus

Sustainability, 2019, vol. 11, issue 12, 1-14

Abstract: Optical remote sensing has been widely used for the identification of archaeological proxies. Such proxies, known as crop or soil marks, can be detected in multispectral images due to their spectral signatures and the distinct contrast that they provide in relation to the surrounding area. The current availability of high-resolution satellite datasets has enabled researchers to provide new methodologies and algorithms that can further enhance archaeological proxies supporting thus image-interpretation. However, a critical point that remains unsolved is the detection of crop and soil marks in non-homogenous environments. In these areas, interpretation is problematic even after the application of sophisticated image enhancement analysis techniques due to the mixed landscape and spectral confusion produced from the high-resolution datasets. To overcome this problem, we propose an image-based methodology in which the vegetation is suppressed following the “forced invariance” method and then we apply a linear orthogonal transformation to the suppressed spectral bands. The new Red–Green–Blue (RGB) image corresponds to a new three-band spectral space where the three axes are linked with the crop mark, vegetation, and soil components. The study evaluates the proposed approach in the archaeological site of “Nea Paphos” in Cyprus using a WorldView-2 multispectral image aiming to overcome the limitations of the mixed environments.

Keywords: remote sensing archaeology; vegetation suppression; orthogonal equations; archaeological proxies; buried archaeological remains; soil marks; crop marks; Cyprus (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
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