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Title: Orthogonal equations for the detection of hidden archaeological remains de-mystified
Authors: Agapiou, Athos 
Keywords: Crop marks;High resolution images;Orthogonal equations;Remote sensing archaeology;Stonehenge site;WorlView-2
Category: Mechanical Engineering
Field: Engineering and Technology
Issue Date: Aug-2017
Publisher: Elsevier Ltd
Source: Journal of Archaeological Science: Reports, 2017, Volume 14, Pages 792-799
Project: ATHENA. Remote Sensing Science Center for Cultural Heritage 
Abstract: Spectral variations of vegetation, known as crop marks, have been widely used for archaeological research as a proxy to detect buried archaeological remains. Such marks can be recognized using space-borne data and image analysis techniques supported by the existing archaeological knowledge of the area under study. Orthogonal equations for the enhancement and detection of crop marks using multispectral satellite images have been recently proposed in the literature. The proposed equations are linear transformations of the initial spectral bands of multispectral datasets aiming to the improvement of the satellite images. For the calculation of the n-space coefficients of this linear transformation a four-step methodology was followed, separately for each sensor. This paper aims to provide the fundamental concept of the development of these equations as well as some aspects related with the application and accuracy assessment. Spectral characteristics of the sensor, atmospheric effects, and spectral calibration of the datasets as well as the selection of the appropriate period for applying these equations for the enhancements of crop marks are also discussed. Such orthogonal equations may be further developed and applied for any kind of sensor either hyperspectral or multispectral for the detection of buried archaeological remains. An example of the applicability of the orthogonal equations at Stonehenge archaeological site is also demonstrated.
ISSN: 2352409X
Rights: © 2016 The Author
Type: Article
Appears in Collections:Άρθρα/Articles

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