Bayesian Denoising of SAR Image
Y. Murali, M. Ganesh Babu · 2011
Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the coherent nature of the scattering phenomenon. This paper proposes a novel Bayesian-based algorithm within the framework of wavelet analysis, which reduces speckle in SAR images while preserving the structural features and textural information of the scene. The speckle noise is one severe obstacle for automatic mapping from radar images. Image de-speckling is used to remove the multiplicative speckle while retaining as much as possible the important signal features. I. Introduction Synthetic aperture radar systems are all-weather, night and day, imaging systems. Automatic interpretation of information in SAR images is very difficult because SAR images are corrupted by a noise called speckle that arises from an imaging device and strongly hinders data interpretation. The speckle noise in SAR images can be removed using an image restoration technique called despeckling. The goal of despeckling is to remove speckle-noise from SAR images and to preserve all images' textural features. The statistical modeling of SAR images has been intensively investigated over recent years. The SAR imaging technique has become popular because of its usability under varied weather conditions, its ability to penetrate through clouds and soil, and due to the independence of SAR image resolution with regard to sensor height. Since SAR systems rely upon coherence properties of the scattered signals, they are highly susceptible to interference effects. A SAR image is a mean intensity estimate of the radar reflectivity of the region being imaged. The difference between a particular measurement and the true mean value is referred to as speckle noise.