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

DC FieldValueLanguage
dc.contributor.authorHaburaj, Vincent
dc.contributor.authorKrause, Jan
dc.contributor.authorPless, Sebastian
dc.contributor.authorWaske, Bjoern
dc.contributor.authorSchuett, Brigitta
dc.date.accessioned2021-12-23T16:20:46Z-
dc.date.available2021-12-23T16:20:46Z-
dc.date.issued2019
dc.identifier.issn00934690
dc.identifier.urihttps://osnascholar.ub.uni-osnabrueck.de/handle/unios/13586-
dc.description.abstractEstablished 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.
dc.description.sponsorshipCluster of Excellence Topoi (The Formation and Transformation of Space and Knowledge in Ancient Civilizations, Research Area A) [EXC264]; Geo.X; 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 (Tubingen) in 2018. Furthermore, we would like to thank the colleagues at Freie Universitat Berlin for their support and inspiration.
dc.language.isoen
dc.publisherROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
dc.relation.ispartofJOURNAL OF FIELD ARCHAEOLOGY
dc.subjectArchaeology
dc.subjectCLASSIFICATION
dc.subjectCOLOR
dc.subjectdigital archaeology
dc.subjectFIELD
dc.subjectfieldwork
dc.subjectlandscape archaeology
dc.subjectORGANIC-CARBON
dc.subjectPIXELS
dc.subjectPROFILE
dc.subjectREFLECTANCE
dc.subjectRGB imaging
dc.subjectSPATIAL-RESOLUTION
dc.subjectspectral imaging
dc.subjectstratigraphy
dc.subjectunsupervised classification
dc.subjectVARIABLES
dc.titleEvaluating the Potential of Semi-Automated Image Analysis for Delimiting Soil and Sediment Layers
dc.typejournal article
dc.identifier.doi10.1080/00934690.2019.1656321
dc.identifier.isiISI:000486958300001
dc.description.volume44
dc.description.issue8
dc.description.startpage538
dc.description.endpage549
dc.contributor.orcid0000-0002-2393-2762
dc.contributor.researcheridC-4456-2017
dc.identifier.eissn20424582
dc.publisher.place2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
dcterms.isPartOf.abbreviationJ. Field Archaeol.
crisitem.author.deptFB 06 - Mathematik/Informatik-
crisitem.author.deptidfb06-
crisitem.author.parentorgUniversität Osnabrück-
crisitem.author.netidWaBj345-
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