A Novel Deeplabv3+ Network for SAR Imagery Semantic Segmentation Based on the Potential Energy Loss Function of Gibbs Distribution
Abstract
:1. Introduction
2. Materials and Methods
2.1. The Structure of Deeplabv3+ Network
2.2. Potential Energy Loss Function Based on the Gibbs Distribution
2.3. Improved Channel Spatial Attention Module (CBAM)
3. Results and Analysis
3.1. Dataset
3.2. Implement Details
3.3. Ablation Study
3.3.1. Designing the Network for SAR Imagery Semantic Segmentation
Network | Time | |||||||
---|---|---|---|---|---|---|---|---|
PSPNet [11] | 60.93% | 90.22% | 83.14% | 47.02% | 53.76% | 40.75% | 54.98% | 3.13s |
FCN [9] | 68.99% | 90.10% | 87.39% | 59.83% | 63.33% | 17.10% | 63.55% | 3.03s |
Deeplabv3+ –Resnet [15] | 72.83% | 92.14% | 87.34% | 85.98% | 73.20% | 0 | 67.73% | 4.52s |
Deeplabv3+ –drn [22] | 70.77% | 91.95% | 89.04% | 84.51% | 70.99% | 0 | 66.94% | 3.65s |
Deeplabv3+ –Mobilenetv2 [16] | 73.37% | 92.51% | 87.38% | 88.36% | 73.18% | 0 | 68.28% | 2.94s |
3.3.2. The Potential Energy Loss Function Based on the Gibbs Distribution
3.3.3. Influence of Weighting Coefficient Compared with RMI Loss Function
3.3.4. The Influence of Improved CBAM
4. Discussion and Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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90.90% | 90.27% | 97.20% | 96.63% | 95.92% | 88.80% | 46.84% | 85.08% |
Network | Time | |||||||
---|---|---|---|---|---|---|---|---|
Deeplabv3+–Resnet | 86.46% | 96.45% | 95.35% | 95.02% | 85.52% | 36.84% | 81.84% | 4.45 s |
Deeplabv3+–drn | 90.80% | 97.19% | 96.69% | 95.46% | 88.99% | 50.32% | 85.73% | 3.82 s |
Deeplabv3+–Mobilenetv2 | 90.27% | 97.18% | 96.85% | 95.64% | 88.60% | 46.69% | 84.99% | 2.83 s |
Method | Time | |||||||
---|---|---|---|---|---|---|---|---|
89.62% | 97.15% | 96.69% | 95.69% | 88.57% | 45.15% | 84.65% | 2.84 s | |
90.27% | 97.18% | 96.85% | 95.64% | 88.60% | 46.69% | 84.99% | 2.83 s | |
89.15% | 97.17% | 96.92% | 95.79% | 88.37% | 42.66% | 84.19% | 2.86 s | |
RMI loss | 88.05% | 96.63% | 96.30% | 95.09% | 86.26% | 37.08% | 82.27% | 2.93 s |
Method | Time | |||||||
---|---|---|---|---|---|---|---|---|
No-CBAM | 90.27% | 97.18% | 96.85% | 95.64% | 88.60% | 46.69% | 84.99% | 2.83 s |
Origin CBAM | 89.24% | 97.09% | 96.90% | 95.38% | 88.06% | 44.64% | 84.41% | 2.86 s |
Improved CBAM | 90.57% | 97.20% | 96.63% | 95.92% | 88.80% | 46.84% | 85.08% | 2.94 s |
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Kong, Y.; Liu, Y.; Yan, B.; Leung, H.; Peng, X. A Novel Deeplabv3+ Network for SAR Imagery Semantic Segmentation Based on the Potential Energy Loss Function of Gibbs Distribution. Remote Sens. 2021, 13, 454. https://doi.org/10.3390/rs13030454
Kong Y, Liu Y, Yan B, Leung H, Peng X. A Novel Deeplabv3+ Network for SAR Imagery Semantic Segmentation Based on the Potential Energy Loss Function of Gibbs Distribution. Remote Sensing. 2021; 13(3):454. https://doi.org/10.3390/rs13030454
Chicago/Turabian StyleKong, Yingying, Yanjuan Liu, Biyuan Yan, Henry Leung, and Xiangyang Peng. 2021. "A Novel Deeplabv3+ Network for SAR Imagery Semantic Segmentation Based on the Potential Energy Loss Function of Gibbs Distribution" Remote Sensing 13, no. 3: 454. https://doi.org/10.3390/rs13030454