Fusion of geophysical images in the study of archaeological sites
Fusion of geophysical images in the study of archaeological sites
In the last few years, the idea of combining images, called image fusion, appeared and it has become a critical area of research and development. Image fusion can be defined as the process of combining images, taken from the same scene and create one single image containing all the essential information of the original images. A single sensor is not always sufficient. Different sensors, effective in different environmental conditions, provide different information of a scene. The underlying idea in this article is to combine geophysical images taken with different sensors, from the same location, aiming to improve the detectability of possible archaeological targets. Three different fusion approaches were used; fusion by calculating the average of the individual images, and through the use of wavelet and curvelet transforms. Furthermore, taking advantage of the curvelet domain we exploit possible prior angle information to enhance the angles where the remnants are expected. We applied the methods in seven different pairs of geophysical images taken from two different archaeological areas. In all cases the fused images produced significantly better results than each of the original geophysical images separately.
archaeology, curvelets, geophysics, image fusion, wavelets
119-133
Karamitrou, Alexandra
25acd266-3030-4958-b5c5-72d4c6b74caf
Bogiatzis, Petros
8fc5767f-51a2-4d3f-aab9-1ee9cfa9272d
Tsokas, Gregory N.
5a5bd98b-3bda-476e-95c5-1de677664a49
1 April 2020
Karamitrou, Alexandra
25acd266-3030-4958-b5c5-72d4c6b74caf
Bogiatzis, Petros
8fc5767f-51a2-4d3f-aab9-1ee9cfa9272d
Tsokas, Gregory N.
5a5bd98b-3bda-476e-95c5-1de677664a49
Karamitrou, Alexandra, Bogiatzis, Petros and Tsokas, Gregory N.
(2020)
Fusion of geophysical images in the study of archaeological sites.
Archaeological Prospection, 27 (2), , [10.1002/arp.1766].
(doi:10.1002/arp.1766).
Abstract
In the last few years, the idea of combining images, called image fusion, appeared and it has become a critical area of research and development. Image fusion can be defined as the process of combining images, taken from the same scene and create one single image containing all the essential information of the original images. A single sensor is not always sufficient. Different sensors, effective in different environmental conditions, provide different information of a scene. The underlying idea in this article is to combine geophysical images taken with different sensors, from the same location, aiming to improve the detectability of possible archaeological targets. Three different fusion approaches were used; fusion by calculating the average of the individual images, and through the use of wavelet and curvelet transforms. Furthermore, taking advantage of the curvelet domain we exploit possible prior angle information to enhance the angles where the remnants are expected. We applied the methods in seven different pairs of geophysical images taken from two different archaeological areas. In all cases the fused images produced significantly better results than each of the original geophysical images separately.
Text
Manuscript_Alexandra_Karamitrou_accepted
- Accepted Manuscript
More information
Accepted/In Press date: 20 December 2019
e-pub ahead of print date: 28 January 2020
Published date: 1 April 2020
Additional Information:
Funding Information:
The authors would like to acknowledge Dr Kenneth L. Kvamme and Dr Eileen G. Ernenwein for making available the data of the SERDP-CS1263 program for Army City. This research has been co-financed by the European Union [European Social Fund (ESF)] and Greek national funds through the Operational Programme ?Education and Lifelong Learning? of the National Strategic Reference Framework (NSRF) ? Research Funding Programme: Heracleitus II. Investing in knowledge society through the ESF.
Publisher Copyright:
© 2020 John Wiley & Sons, Ltd.
Keywords:
archaeology, curvelets, geophysics, image fusion, wavelets
Identifiers
Local EPrints ID: 438016
URI: http://eprints.soton.ac.uk/id/eprint/438016
ISSN: 1075-2196
PURE UUID: 7cf1a6b8-249a-4a8b-8e04-9d3fc9989f31
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Date deposited: 26 Feb 2020 17:30
Last modified: 17 Mar 2024 05:22
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Contributors
Author:
Alexandra Karamitrou
Author:
Petros Bogiatzis
Author:
Gregory N. Tsokas
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