# An Advanced SAR Image Despeckling Method by Bernoulli-Sampling-Based Self-Supervised Deep Learning
## 5 Conclusions  
In this paper, we proposed a novel method for SAR image despeckling, based on self-supervised deep learning and Bernoulli sampling, called SSD-SAR-BS. Our proposed method does not need clean reference images to train the deep despeckling network. Hence, the network can be directly trained on real speckled SAR images. This overcomes the domain gap problem in most of the existing deep-learning-based SAR image despeckling methods; in other words, they adopt supervised learning and use synthetic speckled images rather than real SAR images as the training data. Qualitative and quantitative comparison of synthetic speckled and real SAR images verified the superior performance of our proposed method, compared to the state-of-the-art methods. Our proposed method can suppress most speckle noise and avoid generating artefacts, including the blocking  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Image Size** & **PPB** & **SAR-BM3D** & **ID-CNN** & **SAR-DRN** & **SAR-RDCP** & **\(K=40\)** & **SSD-SAR-BS** & \\ \hline
64 \(\times\) 64 & 1.3955 & 0.6210 & 0.1089 & 0.1066 & 0.1167 & 0.2582 & 0.3751 & 0.4946 & 0.5433 \\
128 \(\times\) 128 & 3.0085 & 2.6615 & 0.1072 & 0.1059 & 0.1165 & 0.7562 & 1.2069 & 1.7837 & 2.2278 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Runtime (seconds) comparison.  
Figure 15: PSNR and SSIM curves of different Bernoulli sampling probabilities \(p\) and different numbers of average times \(K\).  
artefacts caused by SAR-BM3D and the chaotic line-like artefacts caused by supervised deep-learning-based methods trained on synthetic speckled images. In the future, we will consider the combination of transfer learning and multitemporal SAR data (generating approximate clean labels) for SAR despeckling. Furthermore, we plan to explore the despeckling effect for some practical applications using SAR images, such as forest fire burn detection.  
Conceptualization, Y.Y. and Y.J.; methodology, Y.Y. and Y.W. ([PERSON]); validation, Y.Y. and Y.F.; writing--original draft preparation, Y.Y.; writing--review and editing, Y.F., Y.W. ([PERSON]) and L.Z.; supervision, Y.W. ([PERSON]); funding acquisition, Y.W. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Natural Science Foundation of Heilongjiang Province under Grant F2018008 and in part by the Foundation for Distinguished Young Scholars of Harbin under Grant 2017 RAYJ016 and in part by the Natural Science Foundation Joint Guidance Project of Heilongjiang Province under Grant JJ2019 LH2160.  
Not applicable.  
**Data Availability Statement:** UC Merced land-use dataset is available are [[http://vision.ucmerced.edu/datasets](http://vision.ucmerced.edu/datasets)]([http://vision.ucmerced.edu/datasets](http://vision.ucmerced.edu/datasets)) (accessed on 10 March 2021). AID dataset is available at [[https://captain-whu.github.io/AID/](https://captain-whu.github.io/AID/)]([https://captain-whu.github.io/AID/](https://captain-whu.github.io/AID/)) (accessed on 10 March 2021). Sentinel-1 data is available at [[https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)]([https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)) (accessed on 23 October 2020). TerraSAR-X data is available at [[https://tpm-ds.ee.esa.int/oads/access/collection/TerraSAR-X](https://tpm-ds.ee.esa.int/oads/access/collection/TerraSAR-X)]([https://tpm-ds.ee.esa.int/oads/access/collection/TerraSAR-X](https://tpm-ds.ee.esa.int/oads/access/collection/TerraSAR-X)) (accessed on 22 August 2020).  
The authors would like to thank the European Space Agency (ESA) for providing free Sentinel-1 and TerraSAR-X data. The authors would also like to thank the anonymous reviewers for their very competent comments and helpful suggestions.  
The authors declare no conflict of interest.