Poster Session 1
A Simple Sar Speckle Reduction by Wavelet Thresholding
Punya Thitimajshima, Yuttapong Rangsanseri, and Prapon Rakprathanporn
Department of Telecommunications Engineering,
Faculty of Engineering
King Mongkut’s Institute of Technoloyg
Ladkrabang, Bangkok 10520, Thailand
E-mail: [email protected],
This paper describes a method of speckle reduction in Synthetic Aperture Radar (SAR) images based on the wavelet transform. To deduce the problem of filtering the multiplicative noise to the case of an additive noise, the wavelete decomposition is performed on the logarithm of the image gray levels. A threshold value is estimated according to the noise variance and used for the soft-threshold performed on all the high frequency subimages. The filtered logarithmic image is then obtained by reconstruction from the thresholded coefficients. The exponential function on all the high frequency subimages. The filtered logarithmic image is then obtained by reconstruction from the thresholded coefficients. The exponential function of this reconstructed image gives the final filtered image. Experimental results on JERS-1/SAR images showed that the proposed method results in a significant removal.
Synthetic Aperture Radar (SAR) technology has resulted in marked improvements in the spatial resolution images when observing a ground scene from aircraft or satellites, and it can be used to estimate also features like the dampness of the soil, the thickness of a forest, or the roughness of the sea. Nevertheless, SAR images are contaminated by multiplicative noise, due to the coherence of the radar wavelength, labeled as speckle noise which results in an important reduction in the efficiency of target detection and classification algorithms.
Typical noise-smoothing methods are not well suited to preserving edge structures in speckled images. Classical operators are based on the local variance statistics  . The method proposed here starts from a wavelet representation of the image. A few attempts were made at filtering of SAR images by wavelet, essentially filtering can be reduced hence to the case of an additive noise that is mastered in the framework of threshold method930.
The Discrete Wavelet Transform (DWT) is outlined in Section 2. In Section 3, we discuss briefly the method to reduce speckle noise by wavelet thresholding. Section 4 describes the threshold estimation. In Section 5, the experimental results using the proposed algorithm are presented. Finally, section 6 provides a conclusion of the paper.
Discrete wavelet transform (DWT)
The discrete wavelet transform  corresponds to multiresolution approximation expressions. In practice, mutiresolution analysis is carried out using 2 channel filter banks composed of a low-pass (G) and a high-pass (H) filter and each filter bank is then sampled at a half rate (1/2 down sampling) of the previous frequency. By repeating this procedure, it is possible to obtain wavelet transform of any order. The down sampling procedure keeps the scaling parameter constant (n =