SAR Despeckling Model Extracting Dependency Pattern Over GAN-Estimated Speckle and Restricted Gradient-Based Features

With the increasing demand of capturing and processing the visual data of Earth’s surface, synthetic aperture radar (SAR) technology has been widely accepted as the most preferred solution by various organizations. But a major limitation in processing SAR data is its inherent contamination with unwanted random granular interference known as “speckle.” The removal of these unwanted speckle components in order to extract a clear SAR visual, a process known as “despeckling,” forms an important preprocessing task. In this article, we propose a novel model-based technology for the despeckling of SAR visuals contaminated with speckle. The proposed model uses a generative adversarial network (GAN) module for extracting the unwanted speckle component from the input SAR data, which is then analyzed by a convolutional neural network (CNN) module for predicting the noise level (look). The predicted noise level serves as a thresholding parameter for the gradient information extracted from the input SAR data. This eliminates the gradient captured due to the speckle granular effect in the input data. Later the input SAR data, along with the extracted noise and the processed gradient, is fed to a deep dilated CNN-based restoration module to generate a clean SAR visual. Rather than traditional learning of either the residual noisy component or the clean data, the proposed despeckling model learns the pattern in which these noisy components degrade the original data and its gradient information. This methodology, in turn, significantly improves the despeckling performance, when compared with other existing technology in the literature.