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.
Highlights
The use of high-resolution satellite imagery has been widely exploited in the past for the detection of archaeological proxies [1,2,3,4,5,6,7,8]
[17] have used vegetation indices and principal component analysis (PCA) to detect crop marks in the area of Llanera in Spain based on the combined use of data from the WorldView-2 satellite sensor as well as other Red–Green–Blue (RGB) and near infrared images from cameras mounted on an unmanned aerial vehicle (UAV)
This study proposes an image-based approach based on the fundamental work of [22,24] which can enhance archaeological proxies in mixed environments, overcoming a current limitation in remote sensing archaeological research
Summary
The use of high-resolution satellite imagery has been widely exploited in the past for the detection of archaeological proxies [1,2,3,4,5,6,7,8]. The proposed transformation is applicable at any medium and high spatial resolution multispectral dataset as it is a linear combination of the initial spectral bands The importance of such enhancement algorithms, which can improve recognition and identification through contrast enhancement, has been already highlighted by [23], who reported, that remote sensed algorithms might “only work within a consistent background environment and for a specific form of archaeological residue” [23]. The results of all the above-mentioned image analysis techniques in non-homogeneous environments where the background information of the image is mixed with different types of vegetation and soil contrast the results are generally quite poor This is due to the fact that the local contrast of archaeological proxies, as observed in high resolution images, is lost since image noise effectively increases the heterogeneity of local spectra of all targets. The datasets and methods followed are presented below as well as the results and the final conclusions
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