Semantic Segmentation of Urban Buildings from VHR Remote Sensing Imagery Using a Deep Convolutional Neural Network
Abstract
:1. Introduction
2. Methodology
2.1. ResBlock
2.2. Down-Sampling Network
2.3. Up-sampling Network
3. Experiments and Results
3.1. Dataset
3.2. Experimental Setup
3.3. Results
4. Discussion
4.1. About the DeepResUnet
4.2. Effects of Resblock
4.3. Complexity Comparison of Deep Learning Models
4.4. Applicability Analysis of DeepResUnet
4.5. Limitations of Deep Learning Models in This Study
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Name | Kernel Size | Stride | Pad | Output Size |
---|---|---|---|---|
Down-sampling network | ||||
Input | -- | -- | -- | 256 × 256 × 3 |
Conv_1 | 5 × 5 | 1 | 2 | 256 × 256 × 128 |
Pooling_1 | 2 × 2 | 2 | 0 | 128 × 128 × 128 |
ResBlock_1 | 3 × 3/3 × 3/1 × 1 | 1 | 1 | 128 × 128 × 128 |
ResBlock_2 | 3 × 3/3 × 3/1 × 1 | 1 | 1 | 128 × 128 × 128 |
Add_1 | -- | -- | -- | 128 × 128 × 128 |
Pooling_2 | 2 × 2 | 2 | 0 | 64 × 64 × 128 |
ResBlock_3 | 3 × 3/3 × 3/1 × 1 | 1 | 1 | 64 × 64 × 128 |
ResBlock_4 | 3 × 3/3 × 3/1 × 1 | 1 | 1 | 64 × 64 × 128 |
Add_2 | -- | -- | -- | 64 × 64 × 128 |
Pooling_3 | 2 × 2 | 2 | 0 | 32 × 32 × 128 |
ResBlock_5 | 3 × 3/3 × 3/1 × 1 | 1 | 1 | 32 × 32 × 128 |
ResBlock_6 | 3 × 3/3 × 3/1 × 1 | 1 | 1 | 32 × 32 × 128 |
Add_3 | -- | -- | -- | 32 × 32 × 128 |
Pooling_4 | 2 × 2 | 2 | 0 | 16 × 16 × 128 |
ResBlock_7 | 3 × 3/3 × 3/1 × 1 | 1 | 1 | 16 × 16 × 128 |
ResBlock_8 | 3 × 3/3 × 3/1 × 1 | 1 | 1 | 16 × 16 × 128 |
Add_4 | -- | -- | -- | 16 × 16 × 128 |
Up-sampling network | ||||
UpSampling_1 | 2 × 2 | 2 | 0 | 32 × 32 × 128 |
Concat_1 | -- | -- | -- | 32 × 32 × 256 |
Conv_1U | 1 × 1 | 1 | 0 | 32 × 32 × 128 |
ResBlock_1U | 3 × 3/3 × 3/1 × 1 | 1 | 1 | 32 × 32 × 128 |
ResBlock_2U | 3 × 3/3 × 3/1 × 1 | 1 | 1 | 32 × 32 × 128 |
UpSampling_2 | 2 × 2 | 2 | 0 | 64 × 64 × 128 |
Concat_2 | -- | -- | -- | 64 × 64 × 256 |
Conv_2U | 1 × 1 | 1 | 0 | 64 × 64 × 128 |
ResBlock_3U | 3 × 3/3 × 3/1 × 1 | 1 | 1 | 64 × 64 × 128 |
ResBlock_4U | 3 × 3/3 × 3/1 × 1 | 1 | 1 | 64 × 64 × 128 |
UpSampling_3 | 2 × 2 | 2 | 0 | 128 × 128 × 128 |
Concat_3 | -- | -- | -- | 128 × 128 × 256 |
Conv_3U | 1 × 1 | 1 | 0 | 128 × 128 × 128 |
ResBlock_5U | 3 × 3/3 × 3/1 × 1 | 1 | 1 | 128 × 128 × 128 |
ResBlock_6U | 3 × 3/3 × 3/1 × 1 | 1 | 1 | 128 × 128 × 128 |
UpSampling_4 | 2 × 2 | 2 | 0 | 256 × 256 × 128 |
Concat_4 | -- | -- | -- | 256 × 256 × 256 |
Conv_4U | 1 × 1 | 1 | 0 | 256 × 256 × 128 |
ResBlock_7U | 3 × 3/3 × 3/1 × 1 | 1 | 1 | 256 × 256 × 128 |
ResBlock_8U | 3 × 3/3 × 3/1 × 1 | 1 | 1 | 256 × 256 × 128 |
Conv_5U | 1 × 1 | 1 | 0 | 256 × 256 × 2 |
Output | -- | -- | -- | 256 × 256 × 2 |
Models | Precision | Recall | F1 | Kappa | OA |
---|---|---|---|---|---|
FCN-8s [28] | 0.9163 | 0.9102 | 0.9132 | 0.8875 | 0.9602 |
SegNet [29] | 0.9338 | 0.8098 | 0.8674 | 0.8314 | 0.9431 |
DeconvNet [31] | 0.8529 | 0.9001 | 0.8758 | 0.8375 | 0.9413 |
U-Net [30] | 0.8840 | 0.9190 | 0.9012 | 0.8709 | 0.9537 |
ResUNet [51] | 0.9074 | 0.9315 | 0.9193 | 0.8948 | 0.9624 |
DeepUNet [57] | 0.9269 | 0.9245 | 0.9257 | 0.9035 | 0.9659 |
DeepResUnet | 0.9401 | 0.9328 | 0.9364 | 0.9176 | 0.9709 |
Metrics | Baseline + Plain Neural Unit | Baseline + Basic Residual Unit | Baseline + Bottleneck | Baseline + Resblock (DeepResUnet) |
---|---|---|---|---|
Precision | 0.9234 | 0.9329 | 0.9277 | 0.9401 |
Recall | 0.9334 | 0.9330 | 0.9321 | 0.9328 |
F1 | 0.9283 | 0.9329 | 0.9299 | 0.9364 |
Kappa | 0.9068 | 0.9129 | 0.9089 | 0.9176 |
OA | 0.9669 | 0.9691 | 0.9677 | 0.9709 |
Parameters (m) | 4.89 | 4.89 | 3.06 | 2.79 |
Training time (second/epoch) | 1485 | 1487 | 1615 | 1516 |
Inference time (ms/image) | 63.5 | 63.8 | 72.5 | 69.3 |
Model | Parameters (m) | Training Time (Second/Epoch) | Inference Time (ms/image) |
---|---|---|---|
FCN-8s [28] | 134.27 | 979 | 86.1 |
SegNet [29] | 29.46 | 1192 | 60.7 |
DeconvNet [31] | 251.84 | 2497 | 214.3 |
U-Net [30] | 31.03 | 718 | 47.2 |
ResUNet [51] | 8.10 | 1229 | 55.8 |
DeepUNet [57] | 0.62 | 505 | 41.5 |
DeepResUnet | 2.79 | 1516 | 69.3 |
Models | Precision | Recall | F1 | Kappa | OA |
---|---|---|---|---|---|
FCN-8s [28] | 0.8831 | 0.9339 | 0.9078 | 0.8807 | 0.9581 |
SegNet [29] | 0.9475 | 0.6174 | 0.7477 | 0.6944 | 0.9079 |
DeconvNet [31] | 0.8004 | 0.9135 | 0.8532 | 0.8080 | 0.9306 |
U-Net [30] | 0.8671 | 0.8621 | 0.8646 | 0.8263 | 0.9403 |
ResUNet [51] | 0.9049 | 0.8895 | 0.8972 | 0.8683 | 0.9549 |
DeepUNet [57] | 0.8305 | 0.9219 | 0.8738 | 0.8356 | 0.9412 |
DeepResUnet | 0.9101 | 0.9280 | 0.9190 | 0.8957 | 0.9638 |
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Yi, Y.; Zhang, Z.; Zhang, W.; Zhang, C.; Li, W.; Zhao, T. Semantic Segmentation of Urban Buildings from VHR Remote Sensing Imagery Using a Deep Convolutional Neural Network. Remote Sens. 2019, 11, 1774. https://doi.org/10.3390/rs11151774
Yi Y, Zhang Z, Zhang W, Zhang C, Li W, Zhao T. Semantic Segmentation of Urban Buildings from VHR Remote Sensing Imagery Using a Deep Convolutional Neural Network. Remote Sensing. 2019; 11(15):1774. https://doi.org/10.3390/rs11151774
Chicago/Turabian StyleYi, Yaning, Zhijie Zhang, Wanchang Zhang, Chuanrong Zhang, Weidong Li, and Tian Zhao. 2019. "Semantic Segmentation of Urban Buildings from VHR Remote Sensing Imagery Using a Deep Convolutional Neural Network" Remote Sensing 11, no. 15: 1774. https://doi.org/10.3390/rs11151774