Next Article in Journal
An IPCC-Compliant Technique for Forest Carbon Stock Assessment Using Airborne LiDAR-Derived Tree Metrics and Competition Index
Next Article in Special Issue
Ground-Penetrating Radar Mapping Using Multiple Processing and Interpretation Methods
Previous Article in Journal
Sea and Freshwater Ice Concentration from VIIRS on Suomi NPP and the Future JPSS Satellites
Previous Article in Special Issue
Automated Archiving of Archaeological Aerial Images
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(6), 529; doi:10.3390/rs8060529

Posterior Probability Modeling and Image Classification for Archaeological Site Prospection: Building a Survey Efficacy Model for Identifying Neolithic Felsite Workshops in the Shetland Islands

1
Cultural Site Research and Management, 2113 St Paul, Baltimore, MD 21218, USA
2
School of Archaeology, University College Dublin, Belfield, Dublin, Ireland
3
Department of Applied Mathematics and Statistics, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Kenneth L. Kvamme, Clement Atzberger and Prasad S. Thenkabail
Received: 26 February 2016 / Revised: 12 June 2016 / Accepted: 14 June 2016 / Published: 22 June 2016
(This article belongs to the Special Issue Archaeological Prospecting and Remote Sensing)
View Full-Text   |   Download PDF [2872 KB, uploaded 22 June 2016]   |  

Abstract

The application of custom classification techniques and posterior probability modeling (PPM) using Worldview-2 multispectral imagery to archaeological field survey is presented in this paper. Research is focused on the identification of Neolithic felsite stone tool workshops in the North Mavine region of the Shetland Islands in Northern Scotland. Sample data from known workshops surveyed using differential GPS are used alongside known non-sites to train a linear discriminant analysis (LDA) classifier based on a combination of datasets including Worldview-2 bands, band difference ratios (BDR) and topographical derivatives. Principal components analysis is further used to test and reduce dimensionality caused by redundant datasets. Probability models were generated by LDA using principal components and tested with sites identified through geological field survey. Testing shows the prospective ability of this technique and significance between 0.05 and 0.01, and gain statistics between 0.90 and 0.94, higher than those obtained using maximum likelihood and random forest classifiers. Results suggest that this approach is best suited to relatively homogenous site types, and performs better with correlated data sources. Finally, by combining posterior probability models and least-cost analysis, a survey least-cost efficacy model is generated showing the utility of such approaches to archaeological field survey. View Full-Text
Keywords: archaeological prospection; Worldview-2; linear discriminant classification; principal components analysis; stone tool workshops; archaeological survey archaeological prospection; Worldview-2; linear discriminant classification; principal components analysis; stone tool workshops; archaeological survey
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Megarry, W.P.; Cooney, G.; Comer, D.C.; Priebe, C.E. Posterior Probability Modeling and Image Classification for Archaeological Site Prospection: Building a Survey Efficacy Model for Identifying Neolithic Felsite Workshops in the Shetland Islands. Remote Sens. 2016, 8, 529.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top