T. Skrypitsyna,1 V. Kurkov,1 D. Zhuravlev,2 V. Knyaz,3,4 A. Batasova5
1Moscow State Univ. of Geodesy and Cartography (Russian Federation) 2State Historical Museum (Russian Federation) 3FSUE "State Research Institute of Aviation Systems" (Russian Federation) 4Moscow Institute of Physics and Technology (Russian Federation) 5Institute for the History of Material Culture (Russian Federation)
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Advances in unmanned aerial vehicles (UAV) and optical sensors of various types provide new opportunities for collecting and processing a remote sensing data of a new quality. Applying UAVs to acquire high-resolution imagery makes it possible to produce a digital elevation model (DEM) of high quality and resolution. New quality of an available DEMs allows to analyze small details of the land surface and to retrieve valuable information about hidden archaeological content. Our study addresses to creating and analysing of DEM of large-scale and high-resolution for detecting the traces of hidden ancient artefacts at archaeological sites. The survey for acquiring an imagery for this study has been carried out at Taman Peninsula (Russia) as a part of Russian State Historical Museum expedition aimed at studying of the Bosporan Kingdom (VI-I century BC). We presents the developed techniques for UAV imagery processing which provides improved accuracy of photogrammetric 3D measurements comparing with standard photogrammetric image processing by commercial software. These approaches have been developed for interpretation of terrain models for predicting possible spatial distribution of archaeological artefacts. The proposed techniques allows creating large-scaled digital terrain models of the archaeological sites which can serve for more reliable archaeological prediction and accurate geo-positioning of possible findings. It has showed that the developed techniques provide accurate high quality DEM and serve as useful tool for archaeological sites analyses and predictions.
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T. Skrypitsyna, V. Kurkov, D. Zhuravlev, V. Knyaz, A. Batasova, "Study of the hidden ancient anthropogenic landscapes using digital models of microtopography," Proc. SPIE 11533, Image and Signal Processing for Remote Sensing XXVI, 115331F (20 September 2020); https://doi.org/10.1117/12.2572995