Quantitative Analysis of Approaches and Development of Optimal Wavelet based Denoising Technique for SAR Data

Abstract

The importance of microwave and mm-wave sensors in the field of remote sensing has been growing in leaps and bounds over the last three decades, due to their inherent advantages of all weather, day and night operations. These frequencies are recently being used in other fields too, such as, in the fields of surveillance and medical imaging. Signal data from these sensors have different characteristics as compared to optical and IR(Infra Red) sensors. Due to the methods of data acquisition data from microwave and mm-wave sensors, need extensive and complicated signal processing, filtering, and calibration for reconstructing the image and make them useful for further applications. newlineWavelet based denoising and enhancement techniques attempted on SAR (Synthetic Aperture Radar) type of data, can yield encouraging and useful results. Most of the techniques developed using wavelet based concepts have mostly been attempted on optical images and simulated speckle noise or medical images. Many of the recent papers have also illustrated the utility of wavelet based techniques on data sets from missions such as ERS, JERS, and Radarsat. High resolution and multi mode, SAR data from state of the art mission like that of ISRO s RISAT-1(Radar Imaging SATellite)provide ample scope for exploring wavelet based reconstruction techniques and denoising methods. newlineWavelet analysis actually provide a scope to analyse globally but operate locally , which is found to be a very useful technique in the image processing domain of applications such as image compression, denoising, zooming, enhancements etc. Discrete Wavelet Transform is used to transform digital signals in the image to discrete coefficients in the wavelet domain.Wavelet based approaches have the potential to give good results which are comparable to conventional techniques using spatial domain filtering. Since microwave remote sensing sensor s signals are heavily tarnished by noise and RF interferences, it is deemed to be a promising field to study and arrive at s