Poster + Paper
20 September 2020 Study of the hidden ancient anthropogenic landscapes using digital models of microtopography
Author Affiliations +
Conference Poster
Abstract
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.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
T. Skrypitsyna, V. Kurkov, D. Zhuravlev, V. Knyaz, and 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
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Clouds

Roads

Systems modeling

Agriculture

Chemical analysis

Chemical elements

Geodesy

Back to Top