Posterior Probability Modeling and Image Classification for Archaeological Site Prospection: Building a Survey Efficacy Model for Identifying Neolithic Felsite Workshops in the Shetland Islands
"> Figure 1
<p>Northwestern Europe, the Shetland Islands and the North Roe Felsite Project (NRFP) Extent (Images by the Authors).</p> "> Figure 2
<p>A Felsite Workshop at the Beorgs of Uyea. Felsite is visible in blue against Red Ronas Granite (Images by the Authors).</p> "> Figure 3
<p>Graphical posterior probability model (PPM) generation steps.</p> "> Figure 4
<p>Pair Plots for Model 6 components 1, 2 and 3.</p> "> Figure 5
<p>Sample receiver operating characteristic (ROC) curve for Model 7 (with area under the curve (AUC) 0.9611.</p> "> Figure 6
<p>Probability Model 9 and Hybrid surface using Cost-Surface.</p> "> Figure 7
<p>The effects of principal components analysis on 8 Worldview-2 bands, showing workshops at the Beorgs of Uyea.</p> ">
Abstract
:1. Introduction
1.1. The Shetland Islands
1.2. The North Roe Felsite Project (NRFP)
“These rocks occur in Shetland only in the small area…, but material from them has been found throughout the main island in the shape of stone axes and flensing knives. For such implements the toughness and durability of the rock and its capacity to take a clean, sharp edge render it pre-eminently suitable and former inhabitants of the island have evidently been well aware of its source.”
2. Materials and Methods
2.1. Archaeological Predictive Modelling and Direct Detection Protocols
2.2. Project Datasets
2.3. Image Processing Techniques
2.4. Image Classification
2.5. Understanding Access: The Least-Cost Approach
3. Results
3.1. Assessing the Models
3.2. Assessing the Cost-Efficacy of the Study
3.3. Integrating the PPM with Least-Cost Surfaces
4. Discussion
4.1. Posterior Probability Modelling and Archaeological Field Survey
4.2. Imagery and Topography
4.3. PPM and Scale
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interests
References
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Band | Lower Band Edge (nm) | Upper Band Edge (nm) | Centre Wavelength |
---|---|---|---|
Coastal Blue | 396 | 458 | 427 |
Blue | 442 | 515 | 478 |
Green | 506 | 586 | 546 |
Yellow | 584 | 632 | 608 |
Red | 624 | 694 | 659 |
Red Edge | 699 | 749 | 724 |
Near Infrared I | 765 | 901 | 833 |
Near Infrared II | 856 | 1043 | 949 |
Dataset | No. Bands | Source | Resolution |
---|---|---|---|
WV-2 | 8 | Digitalglobe Foundation | 2 m |
BDR | 28 | Using Data from Digitalglobe Foundation | 2 m |
Slope | 1 | Elevation Model from Ordnance Survey | 2 m * |
Skyview | 1 | Elevation Model from Ordnance Survey | 2 m * |
Aspect | 1 | Elevation Model from Ordnance Survey | 2 m * |
Model | No. Bands | Statistic | Datasets |
---|---|---|---|
1 | 8 | Mean | WV-2 |
2 | 8 | Median | WV-2 |
3 | 11 | Mean | WV-2 and Topography |
4 | 11 | Median | WV-2 and Topography |
5 | 28 | Mean | WV-2 BDR |
6 | 28 | Median | WV-2 BDR |
7 | 31 | Mean | WV-2 BDR and Topography |
8 | 31 | Median | WV-2 BDR and Topography |
9 | 36 | Mean | WV-2, WV-2 BDR |
10 | 39 | Mean | WV-2, WV-2 BDR and Topography |
Model | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|---|---|
AUC | 0.9562 | 0.9497 | 0.9562 | 0.9590 | 0.9511 | 0.9520 | 0.9611 | 0.9536 | 0.0576 | 0.9511 |
Model | Statistic | Datasets | KS p-Value | Gain | Gain Area % |
---|---|---|---|---|---|
1 | Mean | WV-2 | 4.2501 × 10−27 | 0.8312 | 5% |
2 | Median | WV-2 | 1.3669 × 10−25 | 0.8786 | 5% |
3 | Mean | WV-2 and Topography | 0.0010 | 0.5202 | 38% |
4 | Median | WV-2 and Topography | 0.0004 | 0.5306 | 32% |
5 | Mean | WV-2 BDR | 1.7723 × 10−23 | 0.8688 | 5% |
6 | Median | WV-2 BDR | 2.5351 × 10−23 | 0.8788 | 6% |
7 | Mean | WV-2 BDR and Topography | 0.0660 | 0.6877 | 28% |
8 | Median | WV-2 BDR and Topography | 0.0590 | 0.7039 | 25% |
9 | Mean and Median | WV-2 and WV-2 BDR | 0.0080 | 0.9365 | 5% |
10 | Mean and Median | WV-2, WV-2 BDR and Topography | 0.0023 | 0.8890 | 5% |
Model | KS p-Value | Gain Statistic |
---|---|---|
Model 9 | 0.0080 | 0.9365 |
Maximum Likelihood | 3.6672 × 10−32 | 0.8383 |
Random Forests (1000) | 1.2831 × 10−05 | 0.8438 |
Random Forests (1000): Top 5 Parameters | 0.0186 | 0.8871 |
Random Forests (1000): Top 5 Parameters and Topography | 0.0221 | 0.8833 |
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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. https://doi.org/10.3390/rs8060529
Megarry WP, Cooney G, Comer DC, Priebe CE. 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 Sensing. 2016; 8(6):529. https://doi.org/10.3390/rs8060529
Chicago/Turabian StyleMegarry, William P., Gabriel Cooney, Douglas C. Comer, and Carey E. Priebe. 2016. "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 Sensing 8, no. 6: 529. https://doi.org/10.3390/rs8060529