# An Advanced SAR Image Despeckling Method by Bernoulli-Sampling-Based Self-Supervised Deep Learning
## 1 Introduction  
Remote sensing big data have pushed the research of Earth science considerably and produced a significant amount of Earth observation data. As one of the main sources of remote sensing big data, synthetic aperture radar (SAR) can provide the capability of acquiring all-day and all-weather Earth ground images. Hence, it has played a crucial role in remote sensing big data applications, including wetland monitoring [1, 2], forest assessment [3, 4], snowmelt monitoring [5], flood inund mapping [6], and ship classification and detection [7, 8, 9, 10]. However, it is not easy to extract analysis results from SAR observation big data. SAR images are essentially corrupted by the speckle noise caused by the constructive or destructive interference of back-scattered microwave signals [11]. The existence of speckle noise leads to the degradation of image quality and makes it challenging to interpret SAR images visually and automatically. Hence, suppressing speckle noise (i.e., despeckling) is an indispensable task in SAR image preprocessing. To mitigate the speckle noise in SAR images, a large number of traditional methods have been proposed, including filter-based (e.g., [PERSON] [12] and Frost [13]), variational-based (e.g., the AA model [14] and the SO model [15]), and nonlocal-based (e.g., theprobabilistic patch-based algorithm (PPB) [16] and the SAR block-matching 3D algorithm (SAR-BM3D) [17]). Most of these methods face several significant problems: (1) they usually require a proper selection of the parameter settings, which largely depends on subjective experience; (2) to a certain extent, their performance is scene-dependent. In other words, speckle noise is smoothly removed in homogeneous regions (e.g., agricultural fields), and detailed information (e.g., edges and textures) is lost in heterogeneous regions (e.g., strong scatterers); (3) there are sometimes artefacts in flat areas, such as the ringing near edges and isolated patterns [18]. The detailed analysis of these traditional methods can be found in a review [18]. With the fast advancement of deep learning technology, convolutional neural networks (CNNs) have demonstrated superior computer vision performance, such as image reconstruction [19], semantic segmentation [20], super-resolution [21], object detection [22], and image classification [23] and identification [24]. Benefiting from its powerful feature extraction capability, the CNN has also been employed to achieve image denoising [25]. Generally, CNN-based image denoising methods adopt supervised learning, where a large amount of \"Noisy2 Clean\" image pairs (i.e., noisy inputs and the corresponding clean targets) are needed. Moreover, by minimizing a distance metric between noisy inputs and clean targets, CNN models are trained and updated to output denoised images. However, considering applying supervised-learning-based denoising methods to SAR image despeckling, a key problem arises: it is hard to acquire clean SAR images in real-world conditions. Thus, there will not be sufficient clean SAR images that can be used as targets to train the despeckling network. In the literature, there are mainly two strategies to address this problem: using multitemporal SAR data [26; 27] and synthetic speckled data [28; 29; 30; 31; 32; 33; 34], which are introduced as follows:  
(1) Multitemporal SAR data: [PERSON] et al. [26] trained a 17-layer SAR despeckling CNN (SAR-CNN) using multitemporal data from the same scene, where approximate clean targets were obtained from multitemporal (multilook) SAR images. Similarly, [PERSON] et al. [27] picked out some region images without significant temporal changes (25 dates). Speckled inputs were the first image of each object, and clean targets were obtained from the next series of 25 images.