SDFCNv2: An Improved FCN Framework for Remote Sensing Images Semantic Segmentation
Abstract
:1. Introduction
- 1.
- We design a novel FCN backbone network with hybrid basic convolutional (HBC) blocks and spatial-channel-fusion squeeze-and-excitation (SCFSE) modules for feature recalibration to obtain larger receptive field and reduce network parameters.
- 2.
- We propose the SDFCNv2 framework, which includes a data augmentation method based on spectral-specific stochastic-gamma-transform (SSSGT) to improve generalizability of our model, and a mask-weighted voting decision fusion postprocessing algorithm on overlarge RS image segmentation, in order to balance the final prediction accuracy and computational cost.
2. Related Works
2.1. Receptive Field (RF) of FCNS
2.2. Feature Recalibration Modules
2.3. Sdfcnv1 Model
3. SDFCNv2 Architecture
3.1. Hybrid Basic Convolutional (HBC) Block
3.2. Spatial and Channel Fusion Squeeze-and-Excitation (SCFSE) Module
3.3. Spectral-Specific Stochastic-Gamma-Transform-Based Data Augmentation
- Random scaling in the range of [1, 1.2];
- Random translation by [−5, 5] pixels;
- Random vertically and horizontally flipping.
3.4. Decision Fusion Postprocessing via Mask-Weighted Voting
4. Dataset Construction and the Whole Framework
4.1. Dataset Construction
- 1.
- Data registration. RS images are first aligned with the corresponding label vector data in the identical coordinate system. For optical RS images, we need to further unify their spatial resolution, radiometric resolution and image spectral bands. In this paper, we convert all RS images into 8-bit raster data with a spatial resolution of 0.5 m, containing three spectral bands of red, green, and blue. Besides, we crop the vector data according to the rectangular area of the RS raster data, and we need to filter out the partly overlapping area.
- 2.
- Categorical mapping. We extract the attributes of the category codes from the cropped vector data, convert them into category labels and encode them. (For example we encode ten categories from 0 to 9).
- 3.
- Rasterization. We rasterize the cropped vector data according to the spatial resolution of the RS images and generate labeled raster data, where each pixel value represents a specific category code.
- 4.
- Invalid label cleaning. In full-covered label vector data, invalid labels due to incomplete coverage, edge connection errors, and label errors may appear with extremely low probability. Therefore, it is necessary to classify invalid labels into a certain category for cleaning up.
- 5.
- Data partition. The image–label pairs are divided into training set, validation set, and test set according to the designed proportion.
- 6.
- Generating metadata. In accordance with the partitioned dataset, we need to record the dataset name, partition results, basic attributes of images, categories, file naming rules as metadata to facilitate dataset management and model training.
- 7.
- Manual verification. Check the generated dataset to avoid problems in the above process.
4.2. The Whole Sdfcnv2 Framework
5. Experimental Results and Analysis
5.1. Experiment Datasets
5.2. Model Implementation
- Random rotation by 0°, 90°, 180° or 270°;
- Random vertically and horizontally flipping;
- Random offset (Maximum offset range is consistent with the model input patch size.);
- Selective gamma-transform-based augmentation methods, including GGT, SIGT and SSSGT.
- Overlap fusion strategy with majority voting.
- Mask-weighted voting decision fusion.
- Rotation by four kinds of degrees (0°, 90°, 180° or 270°).
5.3. Experiments on Model Structures and Different Feature Recalibration Modules
5.4. Experiments on Data Augmentation
5.5. Experiments on Decision Fusion via Mask-Weighted Voting
5.6. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Model | # of Parameters | Model File Size |
---|---|---|
FCN-8s | 184,631,780 | 704.39 MB |
PSPNet | 47,346,560 | 181.15 MB |
Deeplab v3+ | 41,254,358 | 158.39 MB |
HRNetV2 | 9,524,696 | 37.12 MB |
SDFCNv1 | 44,705,118 | 170.86 MB |
SDFCNv2 | 18,606,430 | 71.42 MB |
SDFCNv2 + SE | 19,638,622 | 75.47 MB |
SDFCNv2 + scSE | 19,643,998 | 75.59 MB |
SDFCNv2 + SCFSE | 19,638,982 | 75.88 MB |
Dataset | Source | GSD | Spectral Band | Category | Number of Image and Division (Train, Val and Test) |
---|---|---|---|---|---|
Potsdam | Aerial | 0.05 m | IRRGB | 6 | 38 (18, 6, 14) |
Evlab | Satellite & Aerial | 0.1–1 m | RGB | 10 | 45 (28, 9, 8) |
Songxi | Satellite & Aerial | 0.5 m | RGB | 10 | 26 (12, 5, 9) |
Model | Potsdam Dataset | EvLab Dataset | Songxi Dataset | ||||||
---|---|---|---|---|---|---|---|---|---|
OA | K | mIoU | OA | K | mIoU | OA | K | mIoU | |
FCN-8s | 0.7444 | 0.7021 | 0.5586 | 0.4730 | 0.4313 | 0.2067 | 0.8567 | 0.7215 | 0.3456 |
PSPNet | 0.8059 | 0.7687 | 0.6364 | 0.4956 | 0.4647 | 0.2559 | 0.8602 | 0.7307 | 0.3833 |
Deeplab V3+ | 0.7436 | 0.6989 | 0.5263 | 0.5492 | 0.5085 | 0.2733 | 0.7708 | 0.5758 | 0.2042 |
HRNet V2 | 0.8380 | 0.8029 | 0.6584 | 0.5288 | 0.4831 | 0.2453 | 0.8584 | 0.7208 | 0.3448 |
SDFCN V1 | 0.8006 | 0.7615 | 0.6081 | 0.5083 | 0.4641 | 0.2448 | 0.8627 | 0.7280 | 0.3440 |
SDFCNv2 | 0.8473 | 0.8140 | 0.6741 | 0.5773 | 0.5380 | 0.2963 | 0.8461 | 0.6955 | 0.3458 |
SDFCNv2+SE | 0.8419 | 0.8077 | 0.6744 | 0.5631 | 0.5301 | 0.3017 | 0.8657 | 0.7340 | 0.3708 |
SDFCNv2+scSE | 0.8262 | 0.7920 | 0.6685 | 0.4883 | 0.4532 | 0.2496 | 0.8697 | 0.7471 | 0.3621 |
SDFCNv2+SCFSE | 0.8503 | 0.8177 | 0.6782 | 0.5945 | 0.5539 | 0.3208 | 0.8762 | 0.7562 | 0.3980 |
Gamma Augmentation | Potsdam Dataset | EvLab Dataset | Songxi Dataset | ||||||
---|---|---|---|---|---|---|---|---|---|
OA | K | mIoU | OA | K | mIoU | OA | K | mIoU | |
No | 0.8306 | 0.7952 | 0.6531 | 0.5398 | 0.4963 | 0.2623 | 0.8501 | 0.7073 | 0.3153 |
GGT | 0.8287 | 0.7927 | 0.6508 | 0.4572 | 0.4263 | 0.2204 | 0.8336 | 0.6714 | 0.3160 |
SIGT | 0.8261 | 0.7897 | 0.6574 | 0.5181 | 0.4769 | 0.2310 | 0.8718 | 0.7483 | 0.3895 |
SSSGT (ours) | 0.8503 | 0.8177 | 0.6782 | 0.5945 | 0.5539 | 0.3208 | 0.8762 | 0.7562 | 0.3980 |
Overlap Rate | rot90° ×4 | Weigthed-Mask (Ours) | Potsdam Dataset | EvLab Dataset | Songxi Dataset | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|
OA | K | mIoU | OA | K | mIoU | OA | K | mIoU | |||
0% | × | × | 0.8503 | 0.8177 | 0.6782 | 0.5945 | 0.5539 | 0.3208 | 0.8762 | 0.7562 | 0.3980 |
25% | × | × | 0.8573 | 0.8256 | 0.6867 | 0.6016 | 0.5611 | 0.3277 | 0.8784 | 0.7602 | 0.4032 |
× | ✓ | 0.8576 | 0.8259 | 0.6871 | 0.6025 | 0.5619 | 0.3287 | 0.8787 | 0.7608 | 0.4040 | |
✓ | × | 0.8639 | 0.8331 | 0.6973 | 0.6137 | 0.5730 | 0.3380 | 0.8815 | 0.7659 | 0.4096 | |
✓ | ✓ | 0.8642 | 0.8334 | 0.6978 | 0.6145 | 0.5738 | 0.3392 | 0.8818 | 0.7665 | 0.4102 |
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Chen, G.; Tan, X.; Guo, B.; Zhu, K.; Liao, P.; Wang, T.; Wang, Q.; Zhang, X. SDFCNv2: An Improved FCN Framework for Remote Sensing Images Semantic Segmentation. Remote Sens. 2021, 13, 4902. https://doi.org/10.3390/rs13234902
Chen G, Tan X, Guo B, Zhu K, Liao P, Wang T, Wang Q, Zhang X. SDFCNv2: An Improved FCN Framework for Remote Sensing Images Semantic Segmentation. Remote Sensing. 2021; 13(23):4902. https://doi.org/10.3390/rs13234902
Chicago/Turabian StyleChen, Guanzhou, Xiaoliang Tan, Beibei Guo, Kun Zhu, Puyun Liao, Tong Wang, Qing Wang, and Xiaodong Zhang. 2021. "SDFCNv2: An Improved FCN Framework for Remote Sensing Images Semantic Segmentation" Remote Sensing 13, no. 23: 4902. https://doi.org/10.3390/rs13234902