Study On Speckle Reduction Methods For Synthetic Aperture Radar Images

Synthetic aperture radar (SAR) plays an important role in many fields, such asgathering information from the Earth’s environment and detecting target，due to itspower ability to implement daytime and nighttime measurements under all weatherconditions. Like other coherent imaging systems, SAR images acquired from a SARsystem inevitably suffer from the multiplicative random noise called speckle. Thepresence of speckle seriously degrades the visual quality of SAR images and limits theeffectiveness for subsequent interpretation processing technologies, such as featureextracting technology and object tracking technology.Therefore, speckle suppressionhas important significance to improve the imaging quality of SAR images and theinterpretation processing effect.This thesis mainly considers the estimating method of equivalent number of looks(ENL) and the despeckling methods for SAR images. The main contributions of thethesis are summarized as the following four parts:Based on edge strength map (ESM), an unsupervised estimation method for theENL in SAR images is given. First, the ESM is produced by ratio operation ofanisotropic Gaussian kernel (AGK) parallel windows. Second, SAR image is dividedinto several image blocks by simple image partition processing, and the local ESMthresholds for image blocks are obtained by an effective unsupervised estimationmethod. Third, SAR image is divided into homogeneous regions and edge regions byshrinkage ESM. Fourth, each local ENL of all pixels in homogeneous regions isestimated by large irregular window under twice restrictions based on ESM. Fifth,global ENL of SAR image is acquired by histogram statistical method for all local ENLvalues. The experimental results show that the proposed method is effective and stabile.Based on an additive transform noise mode of speckle noise in SAR image, atransform-domain despeckling method is proposed by using undecimated waveletpacket transform(UWPT). First, a SAR image is decomposed into multiple subbands bymulti-level UWPT. Second, the lowpass subband is filtered by self-snake diffusion, andthe filtered lowpass subband was regarded as the local mean of the original SAR imagein wavelet domain. Third, adaptive and shrinkage soft-thresholding filter was applied tothe rest subbands by improved L1-L2optimization based on the local mean. Fourth, thedespeckled image was recovered from the all of filtered subbands by the inverse UWPT.The experimental results show the proposed method has good performance in reducing speckle and preserving the edge of SAR images.Three spatial-domain despeckling methods for SAR images are studied.(1)Spatial-domain despeckling method based on region subdivision is given. First,Gaussian-Gamma-shaped bi-windows with different orientations are applied to estimatethe ESM by virtue of ratio operations. Second, the different regions in SAR image areobtained by the threshold-processing ESM. Third, two improved filters, the Kuan filterand the Sigma filter, are used to smooth speckle in homogeneous and edge regions,respectively. In order to strength the ability to reduce speckle in SAR images, iterationfiltering strategy and variable scale local window are adopted in the proposed method.(2) Spatial-domain despeckling method based on improved Frost filtering is proposed.The new method is based on the negative-exponential and weighted filtering model intranditional Frost filter. The weighting coefficients of the new method are obtained bythe decay factor using several local statistics and are adaptive to the characteristics ofregional distribution of SAR image. Meanwhile, two-stage filtering strategy is used.First, the pre-filtering is executed for reducing speckle in SAR image and estimatingfine local statistics. Second, the fine filtering for original SAR image is carried outbased on the fine local statistics obtained by the pre-filtering.(3) Spatial-domaindespeckling method based on iterative direction filtering is given. First, the ratio ESMand direction information are estimated by Gaussian-Gamma-shaped bi-windows, andanisotropic support domain along the ESM direction is obtained by using the ESM anddirection information to self-adaptively control the AGK in rectangular local window.Second, the decay factor is obtained by combining several local statistics, and thenegative-exponential weighting coefficients produced by the decay factor are adaptiveto the characteristics of regional distribution of SAR image. Third, direction filtering isformed by combining the negative-exponential weighting coefficients and the localwindow with anisotropic support domain and different directions. Lastly, reducingdespeckle in SAR image with edge protection can be realized by iterative operation ofdirection filtering. The experimental results show the proposed three spatial-domaindespeckling methods can effectively reduce speckle while preserving the edges in SARimages.Two anisotropic diffusion despeckling methods for SAR images are conducted.(1)Improved DPAD despeckling method based on mean curvature motion (MCM) isproposed. By embedding the MCM into the classical DPAD and using the coefficientsof Kuan filter to control diffusion intensity of the MCM, the improved DPADdespeckling method is developed. The improved DPAD can effectively smooth speckle near edges and reduce blocking artifacts in homogeneous regions.(2) Despecklingmethod via direction-constrained anisotropic diffusion is introduced. Adirection-constrained anisotropic function is developed by combining the improvedFrost filter and the direction constraint obtained by the local directional ratios (LDRs).By the embedding anisotropic function with direction constraint into the anisotropicdiffusion framework coupled with the MCM, the new despeckling method viadirection-constrained anisotropic diffusion is proposed. The experimental results showthe proposed two anisotropic diffusion despeckling methods can effectively reducespeckle near edges and blocking artifacts in homogeneous regions.