Evaluating the Potential of Semi-Automated Image Analysis for Delimiting Soil and Sediment Layers

ABSTRACT

Established methods for delineating anthropogenic and natural strata during fieldwork are based on the visual and tactile perception of excavators. Modern image analysis techniques can help to ensure objectivity and reproducibility when documenting sections and plana. Within this study we examine the unsupervised classification of digital images as a technique for delimiting layers and identifying stratigraphic features. Assessing the potential of this approach, we exemplarily captured soil profiles with high-contrast stratigraphy, located in the area of a historical vineyard (Brandenburg, Germany). Reproducible analyses were carried out using open-source software, allowing for the future advancement of the methodology utilized and providing a basis for the analysis of more complex stratigraphic sequences. We compare clustering results of high-resolution RGB and hyperspectral images (470–830 nm, 37 bands). Multiple pre-processing and processing steps are carried out to evaluate their influence. Our results render the semi-automatic analysis of RGB images helpful for stratigraphic interpretation.

KEYWORDS:

• fieldwork
• digital archaeology
• unsupervised classification
• stratigraphy
• RGB imaging
• spectral imaging
• landscape archaeology

Acknowledgments

We thank the Cluster of Excellence EXC264 Topoi (The Formation and Transformation of Space and Knowledge in Ancient Civilizations, Research Area A) for funding this research. Additional thanks is expressed to the Leibniz Centre for Agricultural Landscape Research (ZALF), which delivered us detailed information about the examined soil educational trail. Furthermore, we would like to thank Geo.X for a travel grant, which supported the presentation of our first results at CAA-I (Tübingen) in 2018. Furthermore, we would like to thank the colleagues at Freie Universität Berlin for their support and inspiration.

Disclosure statement

The authors declare they have no conflicts of interest.

Notes on Contributors

Vincent Haburaj (M.Sc. 2016, Freie Universität Berlin) is a doctoral research fellow at the Institute of Geographical Sciences, Freie Universität Berlin and the Cluster of Excellence EXC264 Topoi. He conducted this research as part of a doctoral thesis, examining the benefits of vis-NIR spectroscopy for the archaeological record. His research focuses on digital methods in landscape archaeology.

Jan Krause (Ph.D. 2013, Freie Universität Berlin) is the project coordinator of the Cluster of Excellence EXC264 Topoi at Freie Universität Berlin. His research focuses on geomorphology, landscape archaeology, GIS and hydrological modeling.

Sebastian Pless (Dipl.-Ing. 2000, Technical University Berlin) is a project manager at the Institute of Optical Sensor Systems at the Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR) in Berlin, working on security research and applications.

Björn Waske (Ph.D. 2008, University of Bonn) is Professor at the Institute of Computer Science, University of Osnabrück and head of the research group Remote Sensing and Digital Image Analysis. He is concerned with multidisciplinary approaches, with a strong focus on monitoring land use cover and land use cover change.

Brigitta Schütt (Ph.D. 1993, RWTH Aachen) is Professor at the Institute of Geographical Sciences, Freie Universität Berlin and head of the research group Physical Geography. Her current research is concerned with the reconstruction of palaeoenvironments, late quaternary palaeo-climate, environmental history, soil erosion, integrated watershed management, and drylands of the old world.

ORCID

Vincent Haburaj http://orcid.org/0000-0002-2393-2762