کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1035707 943863 2011 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Laboratory and in situ assays of digital image analysis based protocols for biodeteriorated rock and mural paintings recording
موضوعات مرتبط
مهندسی و علوم پایه مهندسی مواد دانش مواد (عمومی)
پیش نمایش صفحه اول مقاله
Laboratory and in situ assays of digital image analysis based protocols for biodeteriorated rock and mural paintings recording
چکیده انگلیسی

Rock art paintings, and in general mural paintings, are one of the many elements of cultural heritage complex systems. As the different elements of a system have diverse spatial positions, spatial recording allows understanding their interactions. Thus, a useful approach to mural paintings recording is to understand it as a microcartography issue, managing each element of the system as a cartographic coverage.The approach implemented emphasizes the utilization of data obtained by remote sensing techniques for extracting different kinds of information susceptible of being analysed, classified and plotted in a differentiate way by means of the possibility of reducing redundant data by Principal Component Analysis (PCA) and the elaboration of false-colour images from uncorrelated bands.A laboratory model was prepared in order to simulate biodeterioration of rock art. The samples were photographically recorded thereafter under different lighting conditions, and PCA applied to the resulting images. False-colour images obtained by combining Principal Component bands allowed us to reach results similar to those of an unsupervised classification. The method has been applied to Roman mural paintings from one of the tombs of Carmona Necropolis, obtaining good results.


► Laboratory models were photographed and the resulting images uncorrelated by PCA.
► False colour images from PCA bands allow recording most elements of the system.
► Real mural paintings were recorded to test the accuracy of the method.
► False colour images from PCA show similar results than unsupervised classifications.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Archaeological Science - Volume 38, Issue 10, October 2011, Pages 2571–2578
نویسندگان
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