info:eu-repo/semantics/article
Automatic detection of mud-wall signatures in ground-penetrating radar data
Fecha
2020-11Registro en:
Bordón, 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
1075-2196
CONICET Digital
CONICET
Autor
Bordón, Pablo
Martinelli, Hilda Patricia
Zabala Medina, Peter
Bonomo, Nestor Eduardo
Ratto, Norma Rosa
Resumen
The 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.