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.