Selecting Optimal Combination of Data Channels for Semantic Segmentation in City Information Modelling (CIM)
Abstract
:1. Introduction
2. Materials and Methodology
2.1. Paradigms for Semantic Segmentation
2.2. Study Materials
2.3. Methodology
2.4. Experiment Arrangement
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Index | Preview | Name | Number of Points | Description | Train/Test |
---|---|---|---|---|---|
1 | bildstein1 | 29,302,501 | church in bildstein | Train | |
2 | bildstein3 | 23,765,246 | church in bildstein | Test | |
3 | bildstein5 | 24,671,679 | church in bildstein | Train | |
4 | domfountain1 | 35,494,386 | cathedral in feldkirch | Train | |
5 | domfountain2 | 35,188,343 | cathedral in feldkirch | Test | |
6 | domfountain3 | 35,049,972 | cathedral in feldkirch | Train | |
7 | untermaederbrunnen1 | 16,658,648 | fountain in balgach | Train | |
8 | untermaederbrunnen3 | 19,767,991 | fountain in balgach | Test | |
9 | neugasse | 50,109,087 | neugasse in st. gallen | Test | |
10 | sg27_1 | 161,044,280 | railroad tracks | Train | |
11 | sg27_2 | 248,351,425 | town square | Train | |
12 | sg27_4 | 280,994,028 | village | Test | |
13 | sg27_5 | 218,269,204 | crossing | Train | |
14 | sg27_9 | 222,908,898 | soccer field | Train | |
15 | sg28_4 | 258,719,795 | town | Train |
Index | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
Combination | 8 Channels | RGB | XYZD | IXYZD | IRGB | IRGBX | IRGBY |
Index | 8 | 9 | 10 | 11 | 12 | 13 | - |
Combination | IRGBZ | IRGBD | RGBX | RGBY | RGBZ | RGBD | - |
Base Channels | Additional Channels | Improvement on mIoU | Improvement on OA |
---|---|---|---|
RGB | +Intensity | 1.95% | 1.43% |
XYZD | +Intensity | 4.47% | 6.06% |
RGBX | +Intensity | 1.87% | 0.63% |
RGBY | +Intensity | 3.17% | 0.21% |
RGBZ | +Intensity | 1.50% | 1.54% |
RGBD | +Intensity | 6.46% | 1.74% |
AVE | 3.24% | 2.01% | |
RGB | +X | −3.69% | −1.52% |
IRGB | +X | −1.90% | −3.85% |
AVE | −2.84% | −2.69% | |
RGB | +Y | −3.62% | −3.60% |
IRGB | +Y | −2.31% | −4.43% |
AVE | −2.97% | −4.05% | |
RGB | +Z | −0.47% | −3.47% |
IRGB | +Z | −0.72% | −3.36% |
AVE | −0.63% | −3.46% | |
RGB | +D | 0.77% | −2.00% |
IRGB | +D | 5.47% | −1.88% |
AVE | 3.08% | −2.00% |
8C | RGB | XYZD | IXYZD | IRGB | IRGBX | IRGBY | IRGBZ | IRGBD | RGBX | RGBY | RGBZ | RGBD | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
U-Net-3 Layer | 22.3% | 29.5% | 12.9% | 18.2% | 31.3% | 31.2% | 27.0% | 29.5% | 37.2% | 27.9% | 23.0% | 25.6% | 31.0% |
U-Net-4 Layer | 24.2% | 34.3% | 6.3% | 17.1% | 35.4% | 32.5% | 36.9% | 34.4% | 41.0% | 30.3% | 25.8% | 36.3% | 34.4% |
ResNet18 | 33.4% | 37.2% | 32.6% | 32.5% | 39.7% | 40.5% | 38.6% | 38.7% | 42.7% | 37.5% | 36.4% | 35.9% | 40.6% |
ResNet50 | 36.6% | 42.6% | 28.5% | 36.2% | 43.6% | 40.6% | 39.0% | 41.7% | 49.0% | 39.0% | 37.1% | 41.6% | 40.9% |
ResNet101 | 37.3% | 44.1% | 33.7% | 37.5% | 45.2% | 40.8% | 40.9% | 44.1% | 50.8% | 40.9% | 39.2% | 42.5% | 42.4% |
Mobilenetv2 | 42.1% | 40.8% | 37.6% | 40.3% | 45.3% | 39.3% | 39.7% | 43.7% | 53.3% | 38.5% | 38.0% | 39.7% | 45.6% |
Xception | 40.3% | 41.6% | 36.4% | 41.2% | 44.0% | 44.0% | 44.5% | 44.6% | 49.3% | 43.1% | 42.1% | 45.8% | 45.2% |
Inception-ResnetV2 | 42.8% | 43.6% | 40.9% | 43.4% | 45.6% | 44.7% | 44.9% | 45.6% | 47.9% | 41.9% | 42.3% | 41.7% | 40.4% |
HRCNet | 43.0% | 44.6% | 40.8% | 43.6% | 45.7% | 44.8% | 43.2% | 46.6% | 53.7% | 42.7% | 42.2% | 46.0% | 45.9% |
8C | RGB | XYZD | IXYZD | IRGB | IRGBX | IRGBY | IRGBZ | IRGBD | RGBX | RGBY | RGBZ | RGBD | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
U-Net-3 Layer | 55.7% | 74.3% | 27.6% | 37.7% | 75.3% | 74.8% | 71.0% | 68.1% | 72.0% | 71.8% | 65.6% | 63.7% | 67.9% |
U-Net-4 Layer | 57.5% | 76.2% | 37.6% | 38.1% | 78.5% | 77.6% | 72.4% | 74.8% | 76.7% | 71.9% | 67.2% | 67.5% | 74.8% |
ResNet18 | 64.3% | 83.1% | 60.8% | 63.6% | 83.7% | 80.7% | 80.7% | 82.2% | 81.3% | 78.8% | 79.9% | 80.0% | 81.6% |
ResNet50 | 72.5% | 84.5% | 54.0% | 66.9% | 85.6% | 81.7% | 81.2% | 81.8% | 82.4% | 81.8% | 79.8% | 84.5% | 82.8% |
ResNet101 | 73.2% | 84.7% | 60.7% | 67.1% | 86.9% | 81.9% | 82.0% | 82.5% | 85.8% | 81.5% | 79.9% | 84.6% | 82.9% |
Mobilenetv2 | 83.2% | 86.0% | 65.4% | 66.8% | 86.7% | 80.0% | 76.8% | 85.8% | 85.4% | 81.1% | 80.7% | 79.9% | 85.8% |
Xception | 70.2% | 87.0% | 64.3% | 70.5% | 87.6% | 83.2% | 85.4% | 82.5% | 86.0% | 83.1% | 87.5% | 84.2% | 86.2% |
Inception-ResnetV2 | 77.1% | 82.5% | 64.4% | 72.0% | 87.0% | 81.2% | 83.0% | 83.9% | 85.3% | 84.7% | 86.2% | 85.0% | 77.4% |
HRCNet | 83.9% | 87.6% | 65.3% | 72.2% | 87.8% | 83.3% | 82.9% | 87.1% | 87.3% | 84.3% | 86.6% | 85.1% | 87.1% |
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
U-Net-3 Layer | IRGBD | IRGB | IRGBX | RGBD | RGB | IRGBZ | RGBX | IRGBY | RGBZ | RGBY | 8C | IXYZD | XYZD |
U-Net-4 Layer | IRGBD | IRGBY | RGBZ | IRGB | IRGBZ | RGBD | RGB | IRGBX | RGBX | RGBY | 8C | IXYZD | XYZD |
ResNet18 | IRGBD | RGBD | IRGBX | IRGB | IRGBZ | IRGBY | RGBX | RGB | RGBY | RGBZ | 8C | XYZD | IXYZD |
ResNet50 | IRGBD | IRGB | RGB | IRGBZ | RGBZ | RGBD | IRGBX | IRGBY | RGBX | RGBY | 8C | IXYZD | XYZD |
ResNet101 | IRGBD | IRGB | RGB | IRGBZ | RGBZ | RGBD | IRGBY | RGBX | IRGBX | RGBY | IXYZD | 8C | XYZD |
Mobilenetv2 | IRGBD | RGBD | IRGB | IRGBZ | 8C | RGB | IXYZD | IRGBY | RGBZ | IRGBX | RGBX | RGBY | XYZD |
Xception | IRGBD | RGBZ | RGBD | IRGBZ | IRGBY | IRGB | IRGBX | RGBX | RGBY | RGB | IXYZD | 8C | XYZD |
Inception-ResnetV2 | IRGBD | IRGB | IRGBZ | IRGBY | IRGBX | RGB | IXYZD | 8C | RGBY | RGBX | RGBZ | XYZD | RGBD |
HRCNet | IRGBD | IRGBZ | RGBZ | RGBD | IRGB | IRGBX | RGB | IXYZD | IRGBY | 8C | RGBX | RGBY | XYZD |
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
U-Net-3 Layer | IRGB | IRGBX | RGB | IRGBD | RGBX | IRGBY | IRGBZ | RGBD | RGBY | RGBZ | 8C | IXYZD | XYZD |
U-Net-4 Layer | IRGB | IRGBX | IRGBD | RGB | IRGBZ | RGBD | IRGBY | RGBX | RGBZ | RGBY | 8C | IXYZD | XYZD |
ResNet18 | IRGB | RGB | IRGBZ | RGBD | IRGBD | IRGBX | IRGBY | RGBZ | RGBY | RGBX | 8C | IXYZD | XYZD |
ResNet50 | IRGB | RGB | RGBZ | RGBD | IRGBD | IRGBZ | RGBX | IRGBX | IRGBY | RGBY | 8C | IXYZD | XYZD |
ResNet101 | IRGB | IRGBD | RGB | RGBZ | RGBD | IRGBZ | IRGBY | IRGBX | RGBX | RGBY | 8C | IXYZD | XYZD |
Mobilenetv2 | IRGB | RGB | IRGBZ | RGBD | IRGBD | 8C | RGBX | RGBY | IRGBX | RGBZ | IRGBY | IXYZD | XYZD |
Xception | IRGB | RGBY | RGB | RGBD | IRGBD | IRGBY | RGBZ | IRGBX | RGBX | IRGBZ | IXYZD | 8C | XYZD |
Inception-ResnetV2 | IRGB | RGBY | IRGBD | RGBZ | RGBX | IRGBZ | IRGBY | RGB | IRGBX | RGBD | 8C | IXYZD | XYZD |
HRCNet | IRGB | RGB | IRGBD | RGBD | IRGBZ | RGBY | RGBZ | RGBX | 8C | IRGBX | IRGBY | IXYZD | XYZD |
Year | mIoU (%) | OA (%) | Man-Made | Natural | High Veg | Low Veg | Buildings | Hard Scape | Scanning Art | Cars | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Point/discretization-based Methods | SEGCloud [32] | 2017 | 61.3 | 88.1 | 83.9 | 66.0 | 86.0 | 40.5 | 91.1 | 30.9 | 27.5 | 64.3 |
RF MSSF [33] | 2018 | 62.7 | 90.3 | 87.6 | 80.3 | 81.8 | 36.4 | 92.2 | 24.1 | 42.6 | 56.6 | |
Edge-Con. [34] | 2019 | 59.5 | 87.9 | 84.5 | 70.9 | 76.6 | 26.1 | 91.4 | 18.6 | 56.5 | 51.4 | |
ShellNet [35] | 2019 | 69.3 | 93.2 | 96.3 | 90.4 | 83.9 | 41.0 | 94.2 | 34.7 | 43.9 | 70.2 | |
OctreeNet [36] | 2020 | 59.1 | 89.9 | 90.7 | 82.0 | 82.4 | 39.3 | 90.0 | 10.9 | 31.2 | 46.0 | |
GACNet [37] | 2020 | 70.8 | 91.9 | 86.4 | 77.7 | 88.5 | 60.6 | 94.2 | 37.3 | 43.5 | 77.8 | |
RandLA-Net [18] | 2020 | 77.4 | 94.8 | 95.6 | 91.4 | 86.6 | 51.5 | 95.7 | 51.5 | 69.8 | 76.8 | |
Projection-based Methods | DeePr3SS [16] | 2017 | 58.5 | 88.9 | 85.6 | 83.2 | 74.2 | 32.4 | 89.7 | 18.5 | 25.1 | 59.2 |
SnapNet [19] | 2017 | 59.1 | 88.6 | 82.0 | 77.3 | 79.7 | 22.9 | 91.1 | 18.4 | 37.3 | 64.4 | |
XJTLU(Ours) | 2021 | 63.5 | 89.4 | 85.4 | 74.4 | 74.6 | 31.9 | 93.0 | 25.2 | 41.5 | 82.0 |
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Cai, Y.; Huang, H.; Wang, K.; Zhang, C.; Fan, L.; Guo, F. Selecting Optimal Combination of Data Channels for Semantic Segmentation in City Information Modelling (CIM). Remote Sens. 2021, 13, 1367. https://doi.org/10.3390/rs13071367
Cai Y, Huang H, Wang K, Zhang C, Fan L, Guo F. Selecting Optimal Combination of Data Channels for Semantic Segmentation in City Information Modelling (CIM). Remote Sensing. 2021; 13(7):1367. https://doi.org/10.3390/rs13071367
Chicago/Turabian StyleCai, Yuanzhi, Hong Huang, Kaiyang Wang, Cheng Zhang, Lei Fan, and Fangyu Guo. 2021. "Selecting Optimal Combination of Data Channels for Semantic Segmentation in City Information Modelling (CIM)" Remote Sensing 13, no. 7: 1367. https://doi.org/10.3390/rs13071367
APA StyleCai, Y., Huang, H., Wang, K., Zhang, C., Fan, L., & Guo, F. (2021). Selecting Optimal Combination of Data Channels for Semantic Segmentation in City Information Modelling (CIM). Remote Sensing, 13(7), 1367. https://doi.org/10.3390/rs13071367