Ground-Control Networks for Image Based Surface Reconstruction: An Investigation of Optimum Survey Designs Using UAV Derived Imagery and Structure-from-Motion Photogrammetry
<p>(<b>a</b>) The location of Ennerdale in the UK. (<b>b</b>) An orthomosiac image of the study site.</p> "> Figure 2
<p>A digital surface model (DSM) of the Ennerdale moraines with the positions of ground control points (GCPs) (101 GCPs) and spot heights indicated (530 spot heights). The trimmed survey area displayed here is 0.071 km<sup>2</sup>.</p> "> Figure 3
<p>Ground control point (GCP) locations used to produce the 16 digital surface models (DSMs) reported in <a href="#remotesensing-08-00786-t001" class="html-table">Table 1</a>. The top panel relates to the models that use fewer than 15 GCPs. For example, for a model using 3 GCPs, the GCPs numbered 1, 2 and 3 were applied to the model. For a model using 7 GCPs, the GCPs numbered 1, 2, 3, 4, 5, 6, and 7 were applied to the model.</p> "> Figure 4
<p>(<b>a</b>) The residuals assessed by subtracting a digital surface model (DSM) produced using a uniform distribution of ground control points (GCPs) from a DSM produced using a sub-optimum clustered GCP distribution. Error increases into the decimetre range with distance from the GCP cluster. (<b>b</b>) Polynomial regression of sampled cells (a total of 10,000 values) from the DSM of difference highlighting the influence of GCP distribution on error.</p> ">
Abstract
:1. Introduction
2. Field Site
3. Materials and Methods
3.1. Data Acquisition
3.2. Data Analysis
4. Results
5. Discussion
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Number of GCPs Used | Vertical RMSE (m) | Vertical MAE (m) | Vertical Difference (min; m) | Vertical Difference (max; m) |
---|---|---|---|---|
3 | 0.156 | 0.126 | −0.462 | 0.427 |
4 | 0.064 | 0.049 | −0.206 | 0.291 |
5 | 0.060 | 0.046 | −0.197 | 0.308 |
7 | 0.062 | 0.048 | −0.175 | 0.353 |
8 | 0.073 | 0.058 | −0.192 | 0.257 |
9 | 0.063 | 0.049 | −0.173 | 0.283 |
10 | 0.075 | 0.059 | −0.195 | 0.400 |
15 | 0.076 | 0.061 | −0.179 | 0.270 |
20 | 0.073 | 0.057 | −0.190 | 0.280 |
25 | 0.073 | 0.057 | −0.178 | 0.352 |
30 | 0.067 | 0.052 | −0.177 | 0.269 |
40 | 0.066 | 0.050 | −0.191 | 0.304 |
50 | 0.060 | 0.046 | −0.143 | 0.282 |
60 | 0.064 | 0.047 | −0.160 | 0.548 |
80 | 0.061 | 0.047 | −0.156 | 0.242 |
101 | 0.059 | 0.045 | −0.147 | 0.277 |
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Tonkin, T.N.; Midgley, N.G. Ground-Control Networks for Image Based Surface Reconstruction: An Investigation of Optimum Survey Designs Using UAV Derived Imagery and Structure-from-Motion Photogrammetry. Remote Sens. 2016, 8, 786. https://doi.org/10.3390/rs8090786
Tonkin TN, Midgley NG. Ground-Control Networks for Image Based Surface Reconstruction: An Investigation of Optimum Survey Designs Using UAV Derived Imagery and Structure-from-Motion Photogrammetry. Remote Sensing. 2016; 8(9):786. https://doi.org/10.3390/rs8090786
Chicago/Turabian StyleTonkin, Toby N., and Nicholas G. Midgley. 2016. "Ground-Control Networks for Image Based Surface Reconstruction: An Investigation of Optimum Survey Designs Using UAV Derived Imagery and Structure-from-Motion Photogrammetry" Remote Sensing 8, no. 9: 786. https://doi.org/10.3390/rs8090786