Object-Based Point Cloud Analysis of Full-Waveform Airborne Laser Scanning Data for Urban Vegetation Classification
Abstract
:1 Introduction
2 Related work
3 Test site and data sets
3.1 Test site
3.2 Full-waveform ALS data
3.3 Reference data
4 Methods of the object-based point cloud analysis workflow
4.1 Additional point features
4.2 Segmentation
4.3 Classification tree
4.4 Error assessment
5 Object-based point cloud analysis settings
5.1 Segmentation settings
5.2 Classification tree settings
6 Results and discussion
6.1 Segmentation
6.2 Interpretation of classification tree branches
6.3 Error assessment on the vegetation class
7 Conclusion
Acknowledgments
References
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- 7The absence of single echoes would result in division by 0, which is considered in the implementation.
Parameter | Value |
---|---|
Measurement range | 30 m - 1800 m at target reflectivity of 60% 30 m - 1200 m at target reflectivity of 20% |
Ranging accuracy | 20 mm |
Multi-target resolution | down to 0.5 m |
Measurement rate | 240,000 measurements / sec (burst rate) up to 160,000 measurements / sec (average) |
Scan range | 45° (up to 60°) |
Scan speed | up to 160 lines / sec |
Time stamping | resolution 1 μs, unambiguous range > 1 week |
Laser safety | laser class 1, wavelength near infrared |
Parameter | Settings |
---|---|
Seed criterion (roughness) | All points, descending |
Growing criterion (echo width) | 1 ns (controls dynamic tolerance depending on echo width of starting seed point) |
Nearest neighbors (k) | 5 points |
3D maximum growing distance (dist) | 0.5 m |
Minimum segment size (minArea) | 1 point |
Maximum segment size (maxArea) | 100,000 points |
CT | End nod with Class | SQL WHERE rule | Amount of echoes [%] | ||
---|---|---|---|---|---|
RP | BG | VG | |||
CTew cp=0.01 | branch1: non-veg | density ratiomean >= 0.761 | 65.29 | 86.17 | 67.48 |
echo ratiomean < 0.078 | |||||
branch2: veg | density ratiomean >= 0.761 | 1.03 | 0.18 | 5.27 | |
echo ratiomean >= 0.078 | |||||
branch3: veg | density ratiomean < 0.761 | 33.68 | 13.65 | 27.25 | |
CTew cp=0.004 | branch1: non-veg | density ratiomean >= 0.761 | 65.29 | 86.17 | 67.48 |
echo ratiomean < 0.078 | |||||
branch2: non-veg | density ratiomean < 0.761 | 0.98 | 0.13 | 0.96 | |
echo ratiomean < 0.6335 | |||||
density ratiomean >= 0.4765 | |||||
echo widthmean < 5.769 | |||||
echo widthSD < 0.2455 | |||||
echo ratiomean < 0.423 | |||||
branch3: non-veg | density ratiomean < 0.761 | 0.07 | 0.02 | 0.06 | |
echo ratiomean < 0.6335 | |||||
density ratiomean < 0.4765 | |||||
roughnessmean < 0.1505 | |||||
branch4: veg | density ratiomean >= 0.761 | 1.03 | 0.18 | 5.27 | |
echo ratiomean >= 0.078 | |||||
branch5: veg | density ratiomean < 0.761 | 0.01 | 0 | 0.12 | |
echo ratiomean < 0.6335 | |||||
density ratiomean >= 0.4765 | |||||
echo widthmean < 5.769 | |||||
echo widthSD < 0.2455 | |||||
echo ratiomean >= 0.423 | |||||
branch6: veg | density ratiomean < 0.761 | 0.98 | 0.06 | 1.82 | |
echo ratiomean < 0.6335 | |||||
density ratiomean >= 0.4765 | |||||
echo widthmean < 5.769 | |||||
echo widthSD >= 0.2455 | |||||
branch7: veg | density ratiomean < 0.761 | 0.18 | 0.02 | 0.11 | |
echo ratiomean < 0.6335 | |||||
density ratiomean >= 0.4765 | |||||
echo widthmean >= 5.769 | |||||
branch8: veg | density ratiomean < 0.761 | 10.13 | 2.05 | 6.65 | |
echo ratiomean < 0.6335 | |||||
density ratiomean < 0.4765 | |||||
roughnessmean >= 0.1505 | |||||
branch9: veg | density ratiomean < 0.761 | 21.32 | 11.37 | 17.52 | |
echo ratiomean >= 0.6335 | |||||
CTamplitude | branch1: non-veg | amplitudemean >= 43.64 | 67.07 | 86.31 | 73.72 |
echo ratiomean < 0.391 | |||||
branch2: non-veg | amplitudemean < 43.64 | 0.56 | 0.39 | 0.2 | |
density ratiomean >= 0.9195 | |||||
echo ratiomean < 0.056 | |||||
branch3: veg | amplitudemean >= 43.64 | 0.75 | 0.36 | 0.72 | |
echo ratiomean >= 0.391 | |||||
branch4: veg | amplitudemean < 43.64 | 0.1 | 0.13 | 0.13 | |
density ratiomean >= 0.9195 | |||||
echo ratiomean >= 0.056 | |||||
branch5: veg | amplitudemean < 43.64 | 31.52 | 12.8 | 25.24 | |
density ratiomean < 0.9195 |
RP | BG | VG | |
---|---|---|---|
Number of | |||
total points | 549,944 | 559,963 | 537,945 |
points (outlier removed) | 549,330 | 559,784 | 537,882 |
non-vegetation points in reference | 374,435 | 393,282 | 463,225 |
vegetation points in reference | 175,509 | 166,681 | 74,720 |
Overall accuracy [%] | |||
CTew cp=0.01 | 96.44 | 96.75 | 97.84 |
CTew cp=0.004 | 97.23 | 96.52 | 97.73 |
CTampl | 97.90 | 94.18 | 98.09 |
Average accuracy [%] | |||
CTew cp=0.01 | 97.10 | 96.96 | 95.19 |
CTew cp=0.004 | 97.57 | 97.18 | 95.19 |
CTampl | 97.80 | 91.26 | 95.13 |
Correctness (class vegetation) [%] | |||
CTew cp=0.01 | 91.04 | 92.15 | 92.90 |
CTew cp=0.004 | 93.52 | 90.48 | 92.06 |
CTampl | 97.53 | 95.97 | 95.12 |
Completeness (class vegetation) [%] | |||
CTew cp=0.01 | 98.92 | 97.48 | 91.52 |
CTew cp=0.004 | 98.51 | 98.81 | 91.66 |
CTampl | 97.53 | 84.07 | 91.03 |
© 2008 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).
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Rutzinger, M.; Höfle, B.; Hollaus, M.; Pfeifer, N. Object-Based Point Cloud Analysis of Full-Waveform Airborne Laser Scanning Data for Urban Vegetation Classification. Sensors 2008, 8, 4505-4528. https://doi.org/10.3390/s8084505
Rutzinger M, Höfle B, Hollaus M, Pfeifer N. Object-Based Point Cloud Analysis of Full-Waveform Airborne Laser Scanning Data for Urban Vegetation Classification. Sensors. 2008; 8(8):4505-4528. https://doi.org/10.3390/s8084505
Chicago/Turabian StyleRutzinger, Martin, Bernhard Höfle, Markus Hollaus, and Norbert Pfeifer. 2008. "Object-Based Point Cloud Analysis of Full-Waveform Airborne Laser Scanning Data for Urban Vegetation Classification" Sensors 8, no. 8: 4505-4528. https://doi.org/10.3390/s8084505