Image Despeckling For Synthetic Aperture Radar Using Reinforcement Learning

Synthetic aperture radar (SAR) is an imaging mechanism that provides a plethora of information for a large number of applications ranging from surveillance to research. It represents a distinct advancement over the optical spectrum which is known to be highly dependent on the meteorological conditions of the subject[3]. However, the SAR images are subjected to speckle noises. These noises present a challenge to the analysis and interpretation of the images[6]. Several despeckling methods that provide an effective solution to the speckle noises often compromise the quality of the images, especially in heterogeneous terrains. This paper lays the groundwork for a Machine learning approach to the speckle problem. The method implements a Reinforcement learning model, that works by implementing a Q-algorithm, trained to obtain the closest possible pixel value of a speckled image to its clean counterpart[17]. The method is oriented towards formulating a simplified and feasible approach to despeckling SAR images catering to a vast variety of speckle noises and varied speckle patterns and facilitating their applications in various fields.