A Heuristic Method for Power Pylon Reconstruction from Airborne LiDAR Data
<p>Processing flowchart of the proposed method.</p> "> Figure 2
<p>Definition of the 3D pylon coordinate system: (<b>a</b>) the front view of a pylon; and (<b>b</b>) the vertical view.</p> "> Figure 3
<p>Pylon decomposition based on statistical analysis: (<b>a</b>,<b>e</b>) the original point cloud of extracted pylons, and the red planes are the waist plane; (<b>b</b>,<b>c</b>,<b>f</b>,<b>g</b>) statistical analysis results on density and length histogram, respectively; and (<b>d</b>,<b>h</b>) decomposition results.</p> "> Figure 4
<p>Extraction and segmentation of corner points: (<b>a</b>) layering results of the original pylon body point cloud; (<b>b</b>) contour points of one bin extracted by convex hull algorithm (<b>c</b>) simplified corner points; and (<b>d</b>) segmentation results of four subsets which are colored in different color.</p> "> Figure 5
<p>Pylon body reconstruction based on RANSAC: (<b>a</b>) corner points of one subset; (<b>b</b>) corner line fitting result of one subset; (<b>c</b>) fitting results of four corner lines; and (<b>d</b>) refined results of the pylon body. The original point cloud is colored in black.</p> "> Figure 6
<p>3D parametric model library of pylon heads. The parameters are specified as the feature height, feature length and feature width. (<b>a</b>) Model 1; (<b>b</b>) Model 2; (<b>c</b>) Model 3; (<b>d</b>) Model 4.</p> "> Figure 7
<p>Geometric relations. (<b>a</b>) the front view of a pylon; and (<b>b</b>) the side view.</p> "> Figure 8
<p>Point sampling and its shape context for pylon head type recognition: (<b>a</b>) the sampled edge of pylon head point; (<b>b</b>) the sampled edge points of head model; and (<b>c</b>) the target template used to compute the shape context of point <span class="html-italic">p<sub>i</sub></span>.</p> "> Figure 9
<p>Decreasing of Gibbs energy.</p> "> Figure 10
<p>An example area of the original point cloud.</p> "> Figure 11
<p>Original point cloud data of four typical pylons. (<b>a</b>) pylon of Type 1; (<b>b</b>) pylon of Type 2; (<b>c</b>) pylon of Type 3; (<b>d</b>) pylon of Type 4.</p> "> Figure 12
<p>Filled mages of Pylon 3, Models 2 and 4: (<b>a</b>) the image of Pylon 3’s head points; (<b>b</b>) the image of Model 2; and (<b>c</b>) the image of Model 4.</p> "> Figure 13
<p>Four typical pylon reconstruction results. The pylon body reconstruction results are colored in blue, while the pylon head reconstruction results are colored in yellow. (<b>a</b>,<b>b</b>), (<b>c</b>,<b>d</b>), (<b>e</b>,<b>f</b>) and (<b>g</b>,<b>h</b>) are the reconstructed model of Type 1, Type 2, Type 3 and Type 4, respectively.</p> "> Figure 14
<p>Comparison of the pylon body reconstruction in two situations: (<b>a</b>) the top view of the pylon with vegetation points effect; and (<b>b</b>) the same pylon without vegetation points effect.</p> "> Figure 15
<p>Key point extraction and head reconstruction result: (<b>a</b>–<b>c</b>) the extraction results of alpha = 1 m, 0.5 m, and 0.1 m, respectively; and (<b>d</b>–<b>f</b>) the corresponding head reconstruction results.</p> "> Figure 16
<p>Pylon reconstruction results: (<b>a</b>) the result of the head without data loss; (<b>b</b>) the head with some data loss; and (<b>c</b>) the head data with whole sections of the structure missing.</p> ">
Abstract
:1. Introduction
1.1. Related Work
1.2. Contribution
1.3. Overview
2. Methodology
2.1. Preprocessing
2.2. Pylon Decomposition Based on Statistical Analysis
2.3. Pylon Body Reconstruction Based on a Data-Driven Strategy
2.3.1. Extraction and Segmentation of Corner Points
2.3.2. Corner Line Fitting Based on RANSAC
2.4. Pylon Head Reconstruction Based on a Model-Driven Strategy
2.4.1. 3D Parametric Model Library of Pylon Heads
2.4.2. Pylon Head Type Recognition by Shape Context
2.4.3. Optimizations
3. Experimental Data
4. Results
4.1. Decomposition Results of Different Pylon Types
4.2. Precision of Pylon Body Reconstruction
4.3. Recognition of Pylon Head Type
4.4. Precision of Pylon Head Reconstruction
4.5. Efficiency of Pylon Head Reconstruction
5. Discussion
5.1. Robustness to Noise of Pylon Body Reconstruction
5.2. Influence Factors of Pylon Head Reconstruction
5.2.1. Key Points Extraction
5.2.2. Data Loss
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Appendix A. Detailed Procedure of Simulated Annealing Algorithm
Input: space X, original state x0, initial temperature T, iteration number N at each temperature and the cooling rate γ |
While T > Tmin |
{ |
For i = 0 to N |
sample x* according to in space X |
if > random (0, 1) |
xi+1 = x*; |
else |
xi+1 = xi; |
T = T*γ; |
} |
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Type | Unknown Parameters | Known Parameters |
---|---|---|
Model 1 | H1 H2 L1 L2 | Length Height tdx tdy H3 L3 |
Model 2 | H1 H2 H3 H4 L1 L2 L3 | Length Height tdx tdy H5 H6 L4 L5 L6 L7 |
Model 3 | H1 H3 H4 H5 H6 L1 L2 L5 L9 | Length Height tdx tdy H2 H7 L3 L4 L5 L6 L7 L8 |
Model 4 | H1 H2 H3 L1 L2 | Length Height tdx tdy H4 H5 L3 L4 L5 |
ALS System | Flying Height | Horizontal Distance | Flying Speed | Scanning Speed | Rate | Accuracy | Data Density |
---|---|---|---|---|---|---|---|
RIEGL VUX-1 | 50 m above the powerline | 30 m to the powerline | 30 km/h | 200 lines/s | 550 khz | 10 mm | 500 pts/m2 |
Accuravy | Type 1 | Type 2 | Type 3 | Type 4 |
---|---|---|---|---|
dh (m) | 0.08 | 0.06 | 0.10 | 0.07 |
Correctness | 90% | 90% | 100% | 100% |
T | N1 | r1 (%) | (m) | N2 | r2 (%) | (m) | N3 | r3 (%) | (m) | N4 | r4 (%) | (m) |
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 163 | 40 | 0.008 | 207 | 74 | 0.005 | 180 | 37 | 0.016 | 197 | 63 | 0.011 |
1 | 148 | 58 | 0.006 | 185 | 65 | 0.007 | 93 | 84 | 0.006 | 133 | 81 | 0.005 |
1 | 120 | 83 | 0.007 | 210 | 71 | 0.005 | 172 | 48 | 0.010 | 209 | 77 | 0.007 |
2 | 132 | 37 | 0.022 | 211 | 76 | 0.004 | 196 | 66 | 0.007 | 149 | 39 | 0.014 |
2 | 128 | 74 | 0.005 | 171 | 81 | 0.007 | 84 | 56 | 0.008 | 140 | 61 | 0.014 |
2 | 188 | 71 | 0.008 | 210 | 74 | 0.008 | 121 | 48 | 0.022 | 200 | 42 | 0.015 |
3 | 115 | 50 | 0.013 | 217 | 57 | 0.011 | 196 | 54 | 0.011 | 201 | 46 | 0.015 |
3 | 112 | 57 | 0.012 | 234 | 60 | 0.003 | 227 | 48 | 0.010 | 175 | 31 | 0.022 |
3 | 248 | 58 | 0.006 | 414 | 69 | 0.007 | 360 | 50 | 0.005 | 398 | 44 | 0.010 |
4 | 91 | 80 | 0.007 | 131 | 74 | 0.007 | 107 | 83 | 0.005 | 142 | 58 | 0.019 |
4 | 131 | 44 | 0.016 | 293 | 52 | 0.007 | 286 | 41 | 0.013 | 327 | 41 | 0.013 |
4 | 204 | 48 | 0.008 | 269 | 45 | 0.013 | 188 | 49 | 0.023 | 179 | 34 | 0.010 |
Pylon Body | Type | The Front Plane (m) | The Side Plane (m) |
---|---|---|---|
1 | 1 | 0.061 | 0.052 |
2 | 1 | 0.041 | 0.062 |
3 | 1 | 0.037 | 0.056 |
4 | 2 | 0.038 | 0.090 |
5 | 2 | 0.059 | 0.074 |
6 | 2 | 0.048 | 0.080 |
7 | 3 | 0.153 | 0.075 |
8 | 3 | 0.064 | 0.109 |
9 | 3 | 0.134 | 0.071 |
10 | 4 | 0.053 | 0.090 |
11 | 4 | 0.028 | 0.051 |
12 | 4 | 0.020 | 0.050 |
Model 1 | Model 2 | Model 3 | Model 4 | Type | |
---|---|---|---|---|---|
Pylon 1 | 0.240 | 0.153 | 0.035 | 0.097 | 3 |
Pylon 2 | 0.047 | 0.068 | 0.105 | 0.117 | 1 |
Pylon 3 | 0.078 | 0.028 | 0.120 | 0.037 | 2 |
Pylon 4 | 0.121 | 0.070 | 0.098 | 0.024 | 4 |
Pylon | Ncount | Dave (m) | Dmax (m) |
---|---|---|---|
1 | 333 | 0.176 | 0.274 |
2 | 440 | 0.231 | 0.283 |
3 | 995 | 0.253 | 0.578 |
4 | 320 | 0.122 | 0.202 |
Pylon | Type | Np | Head Size (Length × Width × Height) | Ncount | Time (s) |
---|---|---|---|---|---|
1 | 1 | 4 | 14.2 m × 7.9 m × 10.4 m | 15,114 | 6 |
2 | 1 | 4 | 13.0 m × 9.2 m × 10.5 m | 22,315 | 6 |
3 | 1 | 4 | 13.9 m × 8.7 m × 10.0 m | 12,520 | 6 |
4 | 4 | 5 | 11.0 m × 5.5 m × 12.5 m | 3152 | 6 |
5 | 4 | 5 | 11.1 m × 4.7 m × 12.5 m | 4272 | 7 |
6 | 4 | 5 | 11.0 m × 5.4 m × 12.7 m | 5095 | 7 |
7 | 2 | 7 | 14.0 m × 5.4 m × 12.7 m | 5762 | 10 |
8 | 2 | 7 | 13.7 m × 5.5 m × 13.0 m | 3619 | 11 |
9 | 2 | 7 | 13.7 m × 6.4 m × 13.0 m | 5008 | 12 |
10 | 3 | 9 | 17.9 m × 4.4 m × 28.8 m | 12,821 | 23 |
11 | 3 | 9 | 19.0 m × 7.5 m × 29.5 m | 14,080 | 24 |
12 | 3 | 9 | 21.7 m × 14 m × 29.0 m | 28,331 | 28 |
N1 | r1 (%) | (m) | N2 | r2 (%) | (m) | N3 | r3 (%) | (m) | N4 | r4 (%) | (m) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 163 | 40 | 0.008 | 207 | 74 | 0.005 | 180 | 37 | 0.016 | 197 | 63 | 0.011 |
2 | 120 | 83 | 0.006 | 207 | 73 | 0.007 | 168 | 50 | 0.009 | 207 | 74 | 0.008 |
Alpha (m) | Ncount | Dave (m) | Dmax (m) |
---|---|---|---|
1 | 333 | 0.176 | 0.274 |
0.5 | 439 | 0.177 | 0.322 |
0.1 | 1077 | 0.244 | 0.435 |
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Zhou, R.; Jiang, W.; Huang, W.; Xu, B.; Jiang, S. A Heuristic Method for Power Pylon Reconstruction from Airborne LiDAR Data. Remote Sens. 2017, 9, 1172. https://doi.org/10.3390/rs9111172
Zhou R, Jiang W, Huang W, Xu B, Jiang S. A Heuristic Method for Power Pylon Reconstruction from Airborne LiDAR Data. Remote Sensing. 2017; 9(11):1172. https://doi.org/10.3390/rs9111172
Chicago/Turabian StyleZhou, Ruqin, Wanshou Jiang, Wei Huang, Bo Xu, and San Jiang. 2017. "A Heuristic Method for Power Pylon Reconstruction from Airborne LiDAR Data" Remote Sensing 9, no. 11: 1172. https://doi.org/10.3390/rs9111172