Abstract: Since synthetic aperture radar (SAR) is a coherent system, images acquired by SAR are accompanied with speckle noise which disturbs image interpretation and target classification. This noise is conspicuous in single-look SAR images. Based on the analysis of the local speclke statistics characteristics of single-look SAR image and algorithms of spatial filtering,an adaptive speckle filtering method is presented in this paper. The Kuan filter which is based on MMSE criterion is modefied in the method. One of the focal points of the algorithm is to find the set of pixels which belong to the same homogeneous area within filtering windows adaptively. The local relative standard deviation within filtering windows is und as the crucial parameter while a special neighborhood model is used in the filtering window in order to choose the largest homogeneous area within filtering windows. Firstly the formal relative standard deviation C_x of filtering window is calculated.Then the filtering window is divided into eight mutually exclusive sub-window and the central pixel is the only repetitious pixel in each sub-window. For every sub-window, the local relative standard deviation C_i is calculated. If C_x is large than a threshold C_u,the sub-window with the greatest C_i is removed from filtering window and C_x of remaining pixels in filtering window is recalculated. Repeat the procedure until C_x<C_u. The remaining sub-window are meed as filtering area. The selected filtering area is the largest homogeneous area in filtering window and the Kuan filtering algorithm is adopted within the filtering area. If all sub-window within filtering window are removed, reduce the size of window for the determination of homogeneous area. If the size of window is 3 by 3 pixels, replace the central pixel with the average of the nearest four pixels around it. The method is applied to several single-look ER-1/2 SAR images. The results show that the performance of the approach presented is satisfactory in both speckle filtering and the Preservation of image details, and in generating visually-natural images as well.