SAR-CDCFRN: A novel SAR despeckling approach utilizing correlated dual channel feature-based residual network

Abstract

As a result of the increasing need for capturing and processing visual data of the Earth’s surface, Synthetic Aperture Radar (SAR) technology has been widely embraced by all space research organisations. The primary drawback in the acquired SAR visuals (images) is the presence of unwanted granular noise, called “speckle”, which poses a limitation to their processing and analysis. Therefore removing this unwanted speckle noise from the captured SAR visuals, a process known as despeckling, becomes an important task. This article introduces a new despeckling residual network named SAR-CDCFRN. This network simultaneously extracts speckle components from both the spatial and inverse spatial channels. The extracted features are then correlated by a dual-layer attention block and further processed to predict the distribution of speckle in the input noisy image. The predicted distribution, which is the residual noise, is then mapped with the input noisy SAR data to generate a despeckled output image. Experimental results confirm the superiority of the proposed despeckling model over other existing technologies in the literature.