Pure and Applied Geophysics

, Volume 173, Issue 8, pp 2737–2751 | Cite as

Texture Attribute Analysis of GPR Data for Archaeological Prospection

  • Wenke Zhao
  • Emanuele Forte
  • Michele Pipan


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.


Archaeological prospection gray-level co-occurrence matrix (GLCM) ground-penetrating radar (GPR) texture attribute analysis 



We gratefully acknowledge the support of the International Centre for Theoretical Physics (ICTP, Trieste, Italy) Training on Research in Italian Laboratories (TRIL) programme, which sponsored the scholarship of the first author. We also thank dGB Earth Sciences for the OpendTect open source seismic data analysis software, and two anonymous reviewers for providing thoughtful and useful suggestions.


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Copyright information

© Springer International Publishing 2016

Authors and Affiliations

  1. 1.Department of Mathematics and GeosciencesUniversity of TriesteTriesteItaly
  2. 2.School of Earth SciencesZhejiang UniversityHangzhouChina

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