SAR Image Despeckling by Deep Neural Networks: from a PreTrained Model to an EndtoEnd Training Strategy
Abstract
:1. Introduction
2. Related Works
2.1. Additive Gaussian Noise Eeduction by Deep Learning
2.2. Speckle Reduction by Deep Learning
3. SAR Despeckling Using CNNs
3.1. Statistics of SAR Images
3.2. Despeckling Using PreTrained CNN Models
3.2.1. Architecture of the CNN
3.2.2. Homomorphic Filtering with a PreTrained CNN
3.2.3. Iterative Filtering with MuLoG and a PreTrained Model
3.3. Despeckling with a CNN Specifically Trained on SAR Images
3.3.1. TrainingSet Generation
3.3.2. Network Architecture and the Effect of the Loss Function
3.3.3. The Training of the Network
3.4. Hybrid Approach: MuLoG + Trained CNN
4. Experimental Results
4.1. Influence of the Loss Function and of the Network Depth
4.2. Quantitative Comparisons on Images with Simulated Speckle
4.3. Despeckling of Real SingleLook SAR Images: How to Handle Correlations
5. Discussion
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Name  ${\mathit{N}}_{\mathbf{out}}$  Configuration 

Layer 1  64  $3\times 3$ CONV, ReLU 
Layer 2 to (D1)  64  $3\times 3$ CONV, Batch Norm., ReLU 
Layer D  1  $3\times 3$ CONV 
Images  Number of Dates  Number of Patches 

Marais 1  45  40194 
Limagne  53  40194 
Saclay  69  7227 
Lely  25  14850 
Rambouillet  69  39168 
Risoul  72  9648 
Marais 2  45  40194 
Algorithm  MuLoG+CNN  MuLoG+CNN (Pretrained on SAR)  SARCNN 

Input  Natural images  SAR dataset  SAR dataset 
Noise type  Gaussian  Gaussian  Speckle 
Architecture  DnCNN, $D=17$  DnCNN, $D=17$  DnCNN, $D=19$ 
Loss function  ${\ell}_{2}$  ${\ell}_{2}$  ${\ell}_{1}$ 
Images  Noisy  SARBM3D  NLSAR  MuLoG+BM3D  MuLoG+CNN  MuLoG+CNN (Pretrained on SAR)  SARCNN 

Marais 1  10.05 ± 0.0141  23.56 ± 0.1335  21.71 ± 0.1258  23.46 ± 0.0794  23.39 ± 0.0608  23.63 ± 0.0678  24.65 ± 0.0860 
Limagne  10.87 ± 0.0469  21.47 ± 0.3087  20.25 ± 0.1958  21.47 ± 0.2177  21.16 ± 0.0249  21.85 ± 0.1273  22.65 ± 0.2914 
Saclay  15.57 ± 0.1342  21.49 ± 0.3679  20.40 ± 0.2696  21.67 ± 0.2445  21.88 ± 0.2195  22.77 ± 0.2403  23.47 ± 0.2276 
Lely  11.45 ± 0.0048  21.66 ± 0.4452  20.54 ± 0.3303  22.25 ± 0.4365  22.17 ± 0.2702  22.97 ± 0.3671  23.79 ± 0.4908 
Rambouillet  8.81 ± 0.0693  23.78 ± 0.1977  22.28 ± 0.1132  23.88 ± 0.1694  23.30 ± 0.1140  23.30 ± 0.1630  24.73 ± 0.0798 
Risoul  17.59 ± 0.0361  29.98 ± 0.2638  28.69 ± 0.2011  30.99 ± 0.3760  30.85 ± 0.1844  31.03 ± 0.2008  31.69 ± 0.2830 
Marais 2  9.70 ± 0.0927  20.31 ± 0.7833  20.07 ± 0.7553  21.59 ± 0.7573  21.00 ± 0.4886  22.12 ± 0.6792  23.36 ± 0.8068 
Average  12.00  23.17  21.99  23.62  23.39  23.95  24.91 
Images  Noisy  SARBM3D  NLSAR  MuLoG+BM3D  MuLoG+CNN  MuLoG+CNN (Pretrained on SAR)  SARCNN 

Marais 1  0.3571 ± 0.0015  0.8053 ± 0.0018  0.7471 ± 0.0029  0.8003 ± 0.0020  0.7955 ± 0.0027  0.8072 ± 0.0024  0.8333 ± 0.0016 
Limagne  0.4060 ± 0.0021  0.8091 ± 0.0027  0.7493 ± 0.0033  0.8011 ± 0.0030  0.8055 ± 0.0027  0.8147 ± 0.0023  0.8327 ± 0.0029 
Saclay  0.5235 ± 0.0019  0.8031 ± 0.0032  0.7478 ± 0.0040  0.7734 ± 0.0034  0.7956 ± 0.0033  0.8156 ± 0.0030  0.8314 ± 0.0024 
Lely  0.3654 ± 0.0013  0.8473 ± 0.0023  0.8062 ± 0.0023  0.8552 ± 0.0025  0.8659 ± 0.0019  0.8703 ± 0.0018  0.8856 ± 0.0019 
Rambouillet  0.2886 ± 0.0017  0.7831 ± 0.0028  0.7364 ± 0.0031  0.7798 ± 0.0029  0.7706 ± 0.0095  0.7821 ± 0.0073  0.8002 ± 0.0026 
Risoul  0.4362 ± 0.0017  0.8306 ± 0.0024  0.7671 ± 0.0028  0.8345 ± 0.0030  0.8291 ± 0.0027  0.8341 ± 0.0024  0.8493 ± 0.0018 
Marais 2  0.2628 ± 0.0017  0.8506 ± 0.0026  0.8222 ± 0.0022  0.8561 ± 0.0025  0.8594 ± 0.0111  0.8677 ± 0.0097  0.8866 ± 0.0025 
Average  0.3771  0.8184  0.7680  0.8143  0.8173  0.8273  0.8460 
Images  SARBM3D  NLSAR  MuLoG+BM3D  MuLoG+CNN  MuLoG+CNN (Pretrained on SAR)  SARCNN 

Sentinel1:  
Marais 1  226.48  165.24  132.30  288.70  210.17  177.72 
Lely  166.60  75.19  349.32  82.24  145.07  289.03 
Rambouillet  262.47  171.42  139.62  413.09  383.81  295.30 
Marais 2  119.99  213.45  84.67  146.33  182.44  206.93 
TerraSARX:  
Saint Gervais  40.01  39.70  39.37  45.18  129.66  59.21 
SARBM3D  NLSAR  MuLoG+BM3D  MuLoG+CNN  SARCNN 

73.89 s  116.28 s  59.82 s  80.43 s  0.19 s 
MuLoG+CNN  Pros 

Cons 
 
SARCNN  Pros 

Cons 

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Dalsasso, E.; Yang, X.; Denis, L.; Tupin, F.; Yang, W. SAR Image Despeckling by Deep Neural Networks: from a PreTrained Model to an EndtoEnd Training Strategy. Remote Sens. 2020, 12, 2636. https://doi.org/10.3390/rs12162636
Dalsasso E, Yang X, Denis L, Tupin F, Yang W. SAR Image Despeckling by Deep Neural Networks: from a PreTrained Model to an EndtoEnd Training Strategy. Remote Sensing. 2020; 12(16):2636. https://doi.org/10.3390/rs12162636
Chicago/Turabian StyleDalsasso, Emanuele, Xiangli Yang, Loïc Denis, Florence Tupin, and Wen Yang. 2020. "SAR Image Despeckling by Deep Neural Networks: from a PreTrained Model to an EndtoEnd Training Strategy" Remote Sensing 12, no. 16: 2636. https://doi.org/10.3390/rs12162636