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|Title:||Fusion of satellite multispectral images based on ground-penetrating radar (GPR) data for the investigation of buried concealed archaeological remains||Authors:||Agapiou, Athos
Hadjimitsis, Diofantos G.
|Keywords:||Enhancement;Fusion;Ground spectroscopy;Ground-penetrating radar (GPR);GeoEye;Geophysics;Remote sensing archaeology||Category:||Earth and Related Environmental Sciences||Field:||Natural Sciences||Issue Date:||1-Jun-2017||Publisher:||MDPI AG||Source:||GEOSCIENCES,Volume: 7, Issue: 2, Article Number: UNSP 40, Published: JUN 2017||metadata.dc.doi:||http://dx.doi.org/10.3390/geosciences7020040||Journal:||Geosciences (Switzerland)||Abstract:||The paper investigates the superficial layers of an archaeological landscape based on the integration of various remote sensing techniques. It is well known in the literature that shallow depths may be rich in archeological remains, which generate different signal responses depending on the applied technique. In this study three main technologies are examined, namely ground-penetrating radar (GPR), ground spectroscopy, and multispectral satellite imagery. The study aims to propose a methodology to enhance optical remote sensing satellite images, intended for archaeological research, based on the integration of ground based and satellite datasets. For this task, a regression model between the ground spectroradiometer and GPR is established which is then projected to a high resolution sub-meter optical image. The overall methodology consists of nine steps. Beyond the acquirement of the in-situ measurements and their calibration (Steps 1–3), various regression models are examined for more than 70 different vegetation indices (Steps 4–5). The specific data analysis indicated that the red-edge position (REP) hyperspectral index was the most appropriate for developing a local fusion model between ground spectroscopy data and GPR datasets (Step 6), providing comparable results with the in situ GPR measurements (Step 7). Other vegetation indices, such as the normalized difference vegetation index (NDVI), have also been examined, providing significant correlation between the two datasets (R = 0.50). The model is then projected to a high-resolution image over the area of interest (Step 8). The proposed methodology was evaluated with a series of field data collected from the Vésztő-Mágor Tell in the eastern part of Hungary. The results were compared with in situ magnetic gradiometry measurements, indicating common interpretation results. The results were also compatible with the preliminary archaeological investigations of the area (Step 9). The overall outcomes document that fusion models between various types of remote sensing datasets frequently used to support archaeological research can further expand the current capabilities and applications for the detection of buried archaeological remains.||URI:||http://ktisis.cut.ac.cy/handle/10488/10542||ISSN:||2076-3263||Rights:||© 2017 by the authors||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
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checked on Dec 13, 2017
checked on Dec 13, 2017
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