Synthetic Aperture Radar Image Despeckling Based on a Deep Learning Network Employing Frequency Domain Decomposition
Abstract
:1. Introduction
2. Related Works
2.1. Statistical Model of SAR Speckle
2.2. Frequency Domain Decomposition Model
3. Methodology
3.1. General Network Architecture
3.2. Network Subblock Structure
3.3. Overall Network Training
4. Results and Discussion
4.1. Experimental Setting
4.2. Comparative Methods and Quantitative Evaluations
4.3. Simulated Data Experiments
4.4. Real SAR Data Experiments
4.5. Ablation Study
4.6. About Runtime and Number of Parameters
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Methods | L = 1 (PSNR/SSIM) | L = 2 (PSNR/SSIM) | L = 4 (PSNR/SSIM) |
---|---|---|---|
PPB | 23.19/0.5790 | 24.81/0.6637 | 26.36/0.7366 |
SAR-BM3D | 24.65/0.6733 | 26.28/0.7407 | 27.90/0.7990 |
SAR-POTDF | 23.32/0.6120 | 25.58/0.7086 | 27.61/0.7826 |
FANS | 24.43/0.6601 | 26.22/0.7313 | 27.95/0.7909 |
ID-CNN | 25.21/0.6871 | 26.92/0.7535 | 28.56/0.8073 |
SAR-DRN | 25.42/0.7035 | 27.01/0.7623 | 28.61/0.8124 |
HDRANet | 25.41/0.7010 | 26.83/0.7528 | 28.55/0.8099 |
SAR-RDCP | 25.53/0.7095 | 27.19/0.7690 | 28.72/0.8166 |
SAR-CAM | 25.61/0.7142 | 27.19/0.7693 | 28.71/0.8161 |
SAR−FDD | 26.02/0.7348 | 27.59/0.7864 | 29.15/0.8329 |
SAR−FDD−B | 25.94/0.7263 | 27.48/0.7797 | 28.96/0.8251 |
Sensor | Method | ENL | MoI | MoR | EPD-ROA | ||
---|---|---|---|---|---|---|---|
Region I | Region II | HD | VD | ||||
Noerdlingen | PPB | 137.91 | 340.42 | 0.9624 | 0.9626 | 0.9432 | 0.9511 |
SAR-BM3D | 111.11 | 51.51 | 0.9656 | 0.9654 | 0.9366 | 0.9515 | |
SAR-POTDF | 167.94 | 367.51 | 0.9632 | 0.9665 | 0.9319 | 0.9316 | |
FANS | 152.18 | 481.57 | 0.9598 | 0.9634 | 0.9211 | 0.9380 | |
ID-CNN | 163.55 | 179.27 | 0.9615 | 0.9626 | 0.8898 | 0.8923 | |
SAR-DRN | 169.27 | 188.83 | 0.9635 | 0.9643 | 0.9288 | 0.9308 | |
HDRANet | 179.32 | 222.61 | 0.9654 | 0.9661 | 0.9282 | 0.9312 | |
SAR-RDCP | 171.92 | 259.43 | 0.9649 | 0.9656 | 0.9266 | 0.9347 | |
SAR-CAM | 179.52 | 557.32 | 0.9682 | 0.9699 | 0.8883 | 0.9063 | |
SAR−FDD | 174.40 | 177.04 | 0.9675 | 0.9663 | 0.9411 | 0.9395 | |
SAR−FDD−B | 172.01 | 609.34 | 0.9643 | 0.9660 | 0.9040 | 0.9138 | |
Horse track | PPB | 138.42 | 117.20 | 0.9523 | 0.9478 | 0.9469 | 0.9457 |
SAR-BM3D | 86.64 | 39.31 | 0.9587 | 0.9529 | 0.9462 | 0.9731 | |
SAR-POTDF | 112.57 | 84.70 | 0.9712 | 0.9680 | 0.9558 | 0.9555 | |
FANS | 135.09 | 130.45 | 0.9532 | 0.9537 | 0.9268 | 0.9518 | |
ID-CNN | 135.24 | 99.11 | 0.9587 | 0.9540 | 0.9008 | 0.9073 | |
SAR-DRN | 94.83 | 84.75 | 0.9587 | 0.9540 | 0.9479 | 0.9473 | |
HDRANet | 107.91 | 70.47 | 0.9604 | 0.9594 | 0.9386 | 0.9457 | |
SAR-RDCP | 118.20 | 90.98 | 0.9598 | 0.9537 | 0.9415 | 0.9525 | |
SAR-CAM | 127.56 | 117.71 | 0.9611 | 0.9565 | 0.9315 | 0.9581 | |
SAR−FDD | 93.42 | 73.23 | 0.9651 | 0.9567 | 0.9685 | 0.9726 | |
SAR−FDD−B | 342.87 | 494.92 | 0.9588 | 0.9580 | 0.9494 | 0.9700 | |
Volgograd | PPB | 93.28 | 146.67 | 0.9668 | 0.9548 | 0.9256 | 0.9327 |
SAR-BM3D | 94.75 | 115.61 | 0.9747 | 0.9631 | 0.9037 | 0.9269 | |
SAR-POTDF | 179.10 | 102.52 | 0.9911 | 0.9762 | 0.9163 | 0.9276 | |
FANS | 125.86 | 121.59 | 0.9779 | 0.9725 | 0.8998 | 0.9210 | |
ID-CNN | 360.75 | 178.11 | 0.9749 | 0.9603 | 0.8782 | 0.8939 | |
SAR-DRN | 470.37 | 164.41 | 0.9723 | 0.9577 | 0.9019 | 0.9153 | |
HDRANet | 509.75 | 255.59 | 0.9752 | 0.9621 | 0.9059 | 0.9257 | |
SAR-RDCP | 424.98 | 178.73 | 0.9711 | 0.9556 | 0.9030 | 0.9210 | |
SAR-CAM | 754.82 | 284.08 | 0.9746 | 0.9620 | 0.8706 | 0.8953 | |
SAR−FDD | 249.87 | 106.28 | 0.9624 | 0.9533 | 0.9283 | 0.9443 | |
SAR−FDD−B | 954.39 | 849.73 | 0.9656 | 0.9632 | 0.8880 | 0.9082 |
Loss Function | PSNR (dB) | SSIM |
---|---|---|
MSE | 29.1388 | 0.8315 |
DG | 29.1418 | 0.8318 |
29.1357 | 0.8317 | |
29.1423 | 0.8316 | |
29.1501 | 0.8329 |
Method | ID-CNN | SAR-DRN | HDRANet | SAR-RDCP | SAR-CAM | SAR−FDD | SAR- FDDL-B |
---|---|---|---|---|---|---|---|
Parameters | 223,104 | 185,857 | 112,611 | 272,196 | 3,317,284 | 377,537 | 377,537 |
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Zhao, X.; Ren, F.; Sun, H.; Qi, Q. Synthetic Aperture Radar Image Despeckling Based on a Deep Learning Network Employing Frequency Domain Decomposition. Electronics 2024, 13, 490. https://doi.org/10.3390/electronics13030490
Zhao X, Ren F, Sun H, Qi Q. Synthetic Aperture Radar Image Despeckling Based on a Deep Learning Network Employing Frequency Domain Decomposition. Electronics. 2024; 13(3):490. https://doi.org/10.3390/electronics13030490
Chicago/Turabian StyleZhao, Xueqing, Fuquan Ren, Haibo Sun, and Qinghong Qi. 2024. "Synthetic Aperture Radar Image Despeckling Based on a Deep Learning Network Employing Frequency Domain Decomposition" Electronics 13, no. 3: 490. https://doi.org/10.3390/electronics13030490
APA StyleZhao, X., Ren, F., Sun, H., & Qi, Q. (2024). Synthetic Aperture Radar Image Despeckling Based on a Deep Learning Network Employing Frequency Domain Decomposition. Electronics, 13(3), 490. https://doi.org/10.3390/electronics13030490