SAR Image Super Resolution using Importance Sampling Unscented Kalman Filter

Abstract

Synthetic aperture radar (SAR) imaging is a crucial tool in providing images of the earth's surface for military and civilian applications such as target surveillance and its classification. The precision of the application degrades with the presence of inherent speckle and poor resolution of images from the SAR image acquisition devices. Thus, the objective of the proposed method is to develop a technique to enhance the resolution while despeckling the inherent noise, simultaneously, since the conventional super resolution methods have failed to do the same. Moreover, the works from the literature that super resolve SAR images have also neglected the signal-dependent noise model. The work proposed in this paper significantly reduces the speckle and super resolves the SAR image using an Importance Sampling Unscented Kalman Filter framework that best models the non-linearity of the system. The technique has been assessed quantitatively and qualitatively on synthetic images as well as on real SAR images. The performance evaluation based on peak signal-to-noise-ratio, structural similarity index measure, feature similarity index measure, edge preservation factor, and equivalent number of looks values throw light on the superiority of the proposed method over the standard and other recent techniques. This can serve to generate images with a better reconstructive quality that would aid various applications in multidisciplinary domains.