Texture Attribute Analysis of GPR Data for Archaeological Prospection

We evaluate the applicability and the effectiveness of texture attribute analysis of 2-D and 3-D GPR datasets obtained in different archaeological environments. Textural attributes are successfully used in seismic stratigraphic studies for hydrocarbon exploration to improve the interpretation of com...

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Bibliographic Details
Published in:Pure and applied geophysics 2016-08, Vol.173 (8), p.2737-2751
Main Authors: Zhao, Wenke, Forte, Emanuele, Pipan, Michele
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Archaeology
Assessments
Datasets
Earth and Environmental Science
Earth Sciences
Entropy
Excavation
Geophysics/Geodesy
Homogeneity
Matrix
Radar
Stratigraphy
Surface layer
Texture
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Summary:We evaluate the applicability and the effectiveness of texture attribute analysis of 2-D and 3-D GPR datasets obtained in different archaeological environments. Textural attributes are successfully used in seismic stratigraphic studies for hydrocarbon exploration to improve the interpretation of complex subsurface structures. We use a gray-level co-occurrence matrix (GLCM) algorithm to compute second-order statistical measures of textural characteristics, such as contrast, energy, entropy, and homogeneity. Textural attributes provide specific information about the data, and can highlight characteristics as uniformity or complexity, which complement the interpretation of amplitude data and integrate the features extracted from conventional attributes. The results from three archaeological case studies demonstrate that the proposed texture analysis can enhance understanding of GPR data by providing clearer images of distribution, volume, and shape of potential archaeological targets and related stratigraphic units, particularly in combination with the conventional GPR attributes. Such strategy improves the interpretability of GPR data, and can be very helpful for archaeological excavation planning and, more generally, for buried cultural heritage assessment.
ISSN:0033-4553
1420-9136
DOI:10.1007/s00024-016-1355-3