Mobile and Embedded Technology International Conference (MECON - 2013) Proceedings published in International Journal of Computer Applications® (IJCA) (0975 – 8887) 13 A Novel Adaptive Stationary Wavelet-based Technique for SAR Image Despeckling Amlan Jyoti Das Dept. of Electronics & Communication Engineering, Gauhati University, Guwahati- 14, Assam Anjan Kumar Talukdar Dept. of Electronics & Communication Engineering, Gauhati University, Guwahati- 14, Assam Kandarpa Kumar Sarma Dept. of Electronics & Communication Technology, Gauhati University, Guwahati- 14, Assam ABSTRACT In this paper, we present a Stationary Wavelet Transform (SWT) based method for the purpose of despeckling the Synthetic Aperture radar (SAR) images by applying a maximum a posteriori probability (MAP) condition to estimate the noise free wavelet coefficients. A MAP Estimator is designed for this purpose which uses Rayleigh distribution for modeling the speckle noise and Laplacian distribution for modeling the statistics of the noise free wavelet coefficients. The parameters required for MAP estimator is determined by technique used for parameter estimation after SWT. The experimental results show that the proposed despeckling algorithm efficiently removes speckle noise from the SAR images. General Terms SAR image despeckling, Stationary wavelet based MAP estimation technique. Keywords Synthetic aperture radar (SAR), despeckling, Stationary Wavelet Transform (SWT), Maximum a posteriori probability (MAP) Estimator. 1. INTRODUCTION Synthetic aperture radar (SAR) represents a very robust observation tool as it allows the acquisition of high resolution images of different places on the earth. These systems operate under all weather conditions, night and day. SAR images are corrupted by special kind of noise known as speckle noise. In a SAR image, speckle manifests itself similar to thermal noise, by a random pixel-to-pixel variation with statistical properties. Its granular appearance in an SAR image makes it very difficult to visualize the SAR data. Therefore, in many SAR image processing operations like segmentation, speckle filtering is a crucial preprocessing step [1]. Many denoising algorithms have been developed for despeckling SAR images by using the Lee filter [2], the Frost filter [3], LG-MAP filter [13], the Gamma MAP filter [4], and their variations [5], [6]. These filters usually exhibit well in despeckling the SAR images. However, they lack in restoring sharp edge features and details of the original SAR image [7]. Since SAR images are multiplicative in nature, so many wavelet-based despeckling algorithms apply the log-transform to SAR images to statistically convert the multiplicative noise to additive noise prior to applying further denoising technique [7], [8]. An exponential operation is applied to convert the log-transformed images back to the nonlogarithmic format after wavelet denoising [8]. Several solutions have been proposed in the recent years, based on maximum a posteriori probability (MAP) criteria and different distributions: the gamma distribution [9], the α- stable distribution [10], the Pearson system of distributions [11], and the generalized Gaussian (GG) [12], laplacian and gaussian distribution [13] etc. MAP estimator generates a posterior probability by using a prior, likelihood and evidence probability density functions (pdf). The MAP estimate finds a solution for a noise-free image. The speckle noise pdf is approximated by a likelihood pdf of a prior which determines the knowledge of the scene and the best model can be evaluated by maximizing the evidence pdf. In [12], a MAP criterion is derived which is associated with the Generalized Gaussian distribution and is performed in the undecimated wavelet domain. One of the major drawbacks of GG-based MAP solutions is that they can be achieved only numerically, thereby it leads to a high computational cost. In [13], a MAP criterion is derived by considering Gaussian distribution for modeling speckle noise and Laplacian distribution for modeling noise free wavelet coefficients. In [14], the noise-free image was approximated by a Gauss- Markov random field prior and the speckle noise was modeled using Gamma pdf. Although DWT plays a major role in the area of image denoising and image compression, the downsampling operation involved in DWT results in a time-variant translation and has to face difficulties in restoring original image discontinuities in the wavelet domain. Therefore, to restore the translation invariance property, lost by classical DWT, Stationary Wavelet Transform has been preferred in many techniques [11]. In [11], Foucher et al. used the Pearson distribution to model the probability density function (pdf) of SWT wavelet coefficients and reconstructed the despeckled image using the MAP criterion. But the high computational complexity of the Pearson distribution makes this method less appealing in practice, although this algorithm has sound performance. In this paper we propose an efficient SWT based despeckling method by using MAP estimation. We avoid the log- transform and derive a novel MAP estimation criteria based on Rayleigh distribution for modeling the speckle noise and