Filtering Airborne LiDAR Data Through Complementary Cloth Simulation and Progressive TIN Densification Filters
Abstract
:1. Introduction
2. Test Data
3. Methods
3.1. Generation of Initial Provisional DTM Based on Cloth Simulation
3.1.1. Cloth Simulation
3.1.2. Ground Seed Point Acquisition
3.1.3. Initial Provisional DTM Construction
3.2. Parameter Threshold Estimation Based on Statistical Analysis
3.3. Refinement of Initial Provisional DTM Based on Progressive TIN Densification
3.4. Accuracy Indexes
4. Experiments
4.1. Testing with ISPRS Dataset
4.2. Testing with Dense Point Cloud
5. Discussion
5.1. Accuracy of Ground Seed Points
5.2. Parameter Analysis
5.3. Four Filters with Higher Accuracy
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Environment | Site | Sample | Features | Reference (Points) | |
---|---|---|---|---|---|
Ground | Non-Ground | ||||
Urban | 1 | S11 | Mixture of vegetation and buildings on hillside | 21,786 | 16,224 |
S12 | Mixture of vegetation and buildings | 26,691 | 25,428 | ||
2 | S21 | Road with bridge | 10,085 | 2875 | |
S22 | Irregularly shaped buildings and bridge | 22,504 | 10,202 | ||
S23 | Large, irregularly shaped buildings | 13,223 | 11,872 | ||
S24 | Steep slopes | 5434 | 2058 | ||
3 | S31 | Complex buildings | 15,556 | 13,306 | |
4 | S41 | Data gaps, irregularly shaped buildings | 5602 | 5629 | |
S42 | Railway station with trains | 12,443 | 30,027 | ||
Rural | 5 | S51 | Vegetation on hillside | 13,950 | 3895 |
S52 | Steep, terraced slopes | 20,112 | 2362 | ||
S53 | Steep, terraced slopes | 32,989 | 1389 | ||
S54 | Dense buildings | 3983 | 4625 | ||
6 | S61 | Data gaps, discontinuity | 33,854 | 1206 | |
7 | S71 | Underpass and bridge | 13,875 | 1770 |
Samples | Zhang and Lin | Nie et al. | The Proposed Algorithm | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
c(m) | d(m) | c(m) | d(m) | c(m) | d(m) | |||||||
S11 | 20 | 6 | 1.4 | 80 | 20 | 15 | 1.4 | 80 | 1 | 23.37 | >15 | 88.96 |
S12 | 20 | 6 | 1.4 | 80 | 20 | 15 | 1.4 | 80 | 1 | 32.68 | >15 | 85.04 |
S21 | 60 | 6 | 1.4 | 88 | 60 | 15 | 1.4 | 88 | 1 | 23.12 | >15 | 62.71 |
S22 | 60 | 6 | 1.4 | 88 | 60 | 15 | 1.4 | 88 | 1 | 18.21 | >15 | 88.26 |
S23 | 60 | 6 | 1.4 | 88 | 60 | 15 | 1.4 | 88 | 1 | 28.21 | >15 | 85.63 |
S24 | 60 | 6 | 1.4 | 88 | 60 | 15 | 1.4 | 88 | 1 | 25.6 | >15 | 63.37 |
S31 | 35 | 6 | 1.4 | 88 | 35 | 15 | 1.4 | 88 | 1 | 9.33 | >15 | 84.71 |
S41 | 60 | 6 | 1.4 | 88 | 60 | 15 | 1.4 | 88 | 1 | 15.62 | >15 | 62.23 |
S42 | 60 | 6 | 1.4 | 88 | 60 | 15 | 1.4 | 88 | 1 | 16.37 | >15 | 70.76 |
S51 | 10 | 6 | 1 | 70 | 10 | 15 | 1 | 70 | 1 | 18.75 | >15 | 82.42 |
S52 | 10 | 6 | 1 | 70 | 10 | 15 | 1 | 70 | 1 | 22.02 | >15 | 86.37 |
S53 | 10 | 6 | 1 | 70 | 10 | 15 | 1 | 70 | 1 | 27.96 | >15 | 85.92 |
S54 | 10 | 6 | 1 | 70 | 10 | 15 | 1 | 70 | 1 | 16.06 | >15 | 59.33 |
S61 | 40 | 6 | 1.4 | 70 | 40 | 15 | 1.4 | 70 | 1 | 20.64 | >15 | 80.97 |
S71 | 20 | 6 | 1.4 | 70 | 20 | 15 | 1.4 | 70 | 1 | 16.38 | >15 | 62.32 |
Samples | Zhang and Lin | Lin and Zhang | Nie et al. | Shi et al. | Ours |
---|---|---|---|---|---|
S11 | 18.49 | 19.5 | 18.79 | 11.12 | 16.24 |
S12 | 5.92 | 4.78 | 6.62 | 7.17 | 8.85 |
S21 | 4.95 | 6.08 | 5.60 | 6.58 | 14.18 |
S22 | 14.18 | 9.24 | 14.89 | 14.02 | 4.25 |
S23 | 12.06 | 14.43 | 18.08 | 17.43 | 8.52 |
S24 | 20.26 | 5.28 | 24.57 | 13.06 | 15.59 |
S31 | 2.32 | 1.61 | 2.14 | 3.13 | 7.28 |
S41 | 20.44 | 32 | 27.13 | 10.06 | 13.04 |
S42 | 3.94 | 5.95 | 2.42 | 1.91 | 4.75 |
S51 | 5.31 | 4.09 | 2.85 | 12.69 | 3.51 |
S52 | 12.98 | 7.56 | 14.43 | 16.67 | 4.65 |
S53 | 5.58 | 9.9 | 19.37 | 9.77 | 3.95 |
S54 | 6.4 | 10.72 | 4 | 4.99 | 2.58 |
S61 | 16.13 | 6.27 | 6.89 | 7.51 | 0.86 |
S71 | 10.44 | 5.22 | 3.68 | 5.68 | 2.03 |
Avg. | 10.63 | 9.51 | 11.43 | 9.45 | 6.95 |
Samples | Zhang and Lin | Lin and Zhang | Nie et al. | Shi et al. | Ours | |||||
I | II | I | II | I | II | I | II | I | II | |
S11 | 25.67 | 8.84 | 26.28 | 10.4 | 37.24 | 1.35 | 14.74 | 6.07 | 7.51 | 27.98 |
S12 | 8.13 | 3.61 | 6.56 | 3.31 | 11.86 | 1.05 | 11.86 | 1.93 | 4.68 | 13.21 |
S21 | 1.17 | 18.23 | 0.85 | 24.45 | 6.2 | 4.49 | 7.54 | 3.18 | 16.27 | 6.87 |
S22 | 19.05 | 3.44 | 6.43 | 15.44 | 20.82 | 3.6 | 19.44 | 2.15 | 2.22 | 8.73 |
S23 | 19.25 | 4.05 | 23.21 | 4.64 | 35.63 | 1.6 | 29.69 | 3.89 | 3.48 | 14.13 |
S24 | 22.86 | 13.41 | 3.99 | 8.7 | 32.58 | 15.42 | 15.86 | 5.63 | 3.13 | 48.49 |
S31 | 2.1 | 2.59 | 0.54 | 2.59 | 2.02 | 2.41 | 4.61 | 1.43 | 12.74 | 0.91 |
S41 | 39.54 | 1.44 | 62.22 | 1.92 | 52.03 | 0.32 | 17.92 | 2.33 | 25.56 | 0.35 |
S42 | 9.72 | 1.55 | 19.02 | 0.54 | 6.69 | 1.26 | 3.88 | 1.1 | 9.71 | 2.71 |
S51 | 2.05 | 16.97 | 2.22 | 10.81 | 2.9 | 2.77 | 15.14 | 3.79 | 0.07 | 15.81 |
S52 | 12.53 | 16.77 | 6.46 | 16.89 | 16.14 | 2.96 | 17.52 | 9.31 | 0.98 | 35.9 |
S53 | 4.25 | 37.22 | 9.62 | 16.41 | 20.22 | 0.72 | 10.11 | 1.77 | 2.72 | 33.05 |
S54 | 3.59 | 8.82 | 3.16 | 17.23 | 6.76 | 1.78 | 8.65 | 1.87 | 1.16 | 3.81 |
S61 | 16.62 | 2.49 | 6.26 | 6.55 | 8.17 | 2.07 | 7.75 | 0.84 | 0.39 | 13.93 |
S71 | 10.07 | 13.39 | 2.62 | 25.65 | 5.24 | 0.79 | 6 | 3.18 | 0.28 | 15.71 |
Avg. | 13.11 | 10.19 | 11.96 | 11.06 | 17.63 | 2.84 | 12.71 | 3.23 | 4.6 | 11.42 |
Samples | PTDF (Points) | Ours (Points) | OP (%) |
---|---|---|---|
S11 | 116 | 12,863 | 94.75 |
S12 | 158 | 12,135 | 98.95 |
S21 | 10 | 4600 | 99.7 |
S22 | 20 | 9973 | 99.38 |
S23 | 16 | 6391 | 98.18 |
S24 | 10 | 2554 | 98.08 |
S31 | 29 | 7687 | 99.32 |
S41 | 10 | 2310 | 99.65 |
S42 | 20 | 6377 | 94.65 |
S51 | 1008 | 10,247 | 97.3 |
S52 | 1329 | 14,942 | 98.02 |
S53 | 1935 | 23,544 | 99.57 |
S54 | 517 | 2727 | 99.12 |
S61 | 146 | 28,112 | 99.8 |
S71 | 224 | 11,062 | 99.39 |
Avg. | 369.87 | 10,368.27 | 98.39 |
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Cai, S.; Zhang, W.; Liang, X.; Wan, P.; Qi, J.; Yu, S.; Yan, G.; Shao, J. Filtering Airborne LiDAR Data Through Complementary Cloth Simulation and Progressive TIN Densification Filters. Remote Sens. 2019, 11, 1037. https://doi.org/10.3390/rs11091037
Cai S, Zhang W, Liang X, Wan P, Qi J, Yu S, Yan G, Shao J. Filtering Airborne LiDAR Data Through Complementary Cloth Simulation and Progressive TIN Densification Filters. Remote Sensing. 2019; 11(9):1037. https://doi.org/10.3390/rs11091037
Chicago/Turabian StyleCai, Shangshu, Wuming Zhang, Xinlian Liang, Peng Wan, Jianbo Qi, Sisi Yu, Guangjian Yan, and Jie Shao. 2019. "Filtering Airborne LiDAR Data Through Complementary Cloth Simulation and Progressive TIN Densification Filters" Remote Sensing 11, no. 9: 1037. https://doi.org/10.3390/rs11091037