Semi-Automated Detection Of Looting In Afghanistan Using Multispectral Imagery And Principal Component Analysis
Keywords
Afghanistan; heritage preservation; looting; multispectral imagery; principal component analysis; satellite imagery
Abstract
High-resolution satellite imagery has proved to be a powerful tool for calculating the extent of looting at heritage sites in conflict zones around the world. Monitoring damage over time, however, has been largely dependent upon laborious and error-prone manual comparisons of satellite imagery taken at different dates. The semi-automated detection process presented here offers a more expedient and accurate method for monitoring looting activities over time, as evidenced at the site of Ai Khanoum in Afghanistan. It is hoped that this method, which relies upon multispectral imagery and principal component analysis, may be adapted to great effect for use in other areas where heritage loss is of significant concern.
Publication Date
10-1-2017
Publication Title
Antiquity
Volume
91
Issue
359
Number of Pages
1344-1355
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.15184/aqy.2017.90
Copyright Status
Unknown
Socpus ID
85030674853 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85030674853
STARS Citation
Lauricella, Anthony; Cannon, Joshua; Branting, Scott; and Hammer, Emily, "Semi-Automated Detection Of Looting In Afghanistan Using Multispectral Imagery And Principal Component Analysis" (2017). Scopus Export 2015-2019. 5215.
https://stars.library.ucf.edu/scopus2015/5215