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Automatic detection of mud-wall signatures in ground-penetrating radar data

dc.creatorBordón, Pablo
dc.creatorMartinelli, Hilda Patricia
dc.creatorZabala Medina, Peter
dc.creatorBonomo, Nestor Eduardo
dc.creatorRatto, Norma Rosa
dc.date2020-11
dc.date.accessioned2022-04-15T01:55:42Z
dc.date.available2022-04-15T01:55:42Z
dc.identifierhttp://hdl.handle.net/11336/146012
dc.identifierBordón, Pablo; Martinelli, Hilda Patricia; Zabala Medina, Peter; Bonomo, Nestor Eduardo; Ratto, Norma Rosa; Automatic detection of mud-wall signatures in ground-penetrating radar data; John Wiley & Sons Inc; Archaeological Prospection; 28; 1; 11-2020; 89-106
dc.identifier1075-2196
dc.identifierCONICET Digital
dc.identifierCONICET
dc.identifier.urihttp://suquia.ffyh.unc.edu.ar/handle/11336/146012
dc.descriptionThe ground-penetrating radar (GPR) method with the standard constant-offset reflection mode allows to detect and map different types of archaeological structures, such as walls, foundations, floors and roads. The interpretation of the GPR data usually involves a detailed and time-consuming analysis of large amounts of information, which entails nonnegligible chances of errors, especially under nonideal fieldwork conditions. The application of suitable automatic detection algorithms can be useful to more rapidly and successfully complete the interpretation task. In this work, we explore the use of supervised machine learning methodologies to automatically detect mud-wall signatures in radargrams and to map the structures from these detections. Several algorithms, based on Viola–Jones cascade classifiers and the image feature descriptors Haar, histogram of oriented gradients and local binary patterns, were implemented. These algorithms were applied to datasets previously acquired in pre-Inca and Inca-Hispanic sites located in the Andean NW region of Argentina. The best algorithms provided very good detection rates for well-preserved walls and acceptable rates for deteriorated walls, with a low number of spurious predictions. They also exhibited the ability to detect collapsed walls and fragments detached from them. These are remarkable results because mud walls are usually difficult to be detected by conventional analysis, owing to the complex and variable characteristics of their reflection patterns. The results of the automatic detection techniques were represented in plan views and three-dimensional (3D) views that delineated in detail most of the structures of the sites. These algorithms are very fast, so applying them significantly reduces the interpretation times. In addition, once they have been trained using the patterns of one or several sites, they are directly applicable to other sites with similar characteristics.
dc.descriptionFil: Bordón, Pablo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
dc.descriptionFil: Martinelli, Hilda Patricia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina
dc.descriptionFil: Zabala Medina, Peter. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina
dc.descriptionFil: Bonomo, Nestor Eduardo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
dc.descriptionFil: Ratto, Norma Rosa. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto de las Culturas. Universidad de Buenos Aires. Instituto de las Culturas; Argentina
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherJohn Wiley & Sons Inc
dc.relationinfo:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/full/10.1002/arp.1799
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/10.1002/arp.1799
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subjectAUTOMATIC DETECTION
dc.subjectCASCADE CLASSIFIER
dc.subjectGPR
dc.subjectMUD WALL
dc.subjectVIOLA–JONES
dc.subjecthttps://purl.org/becyt/ford/1.5
dc.subjecthttps://purl.org/becyt/ford/1
dc.subjecthttps://purl.org/becyt/ford/6.1
dc.subjecthttps://purl.org/becyt/ford/6
dc.subjecthttps://purl.org/becyt/ford/1.2
dc.subjecthttps://purl.org/becyt/ford/1
dc.titleAutomatic detection of mud-wall signatures in ground-penetrating radar data
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:ar-repo/semantics/artículo
dc.typeinfo:eu-repo/semantics/publishedVersion


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