Recently, superpixel-based methods have shown promising performance for synthetic aperture radar (SAR) image interpretation. In these methods, the statistical model-based local iterative clustering represents the mainstream of superpixel generation for SAR images. However, errors in the model parameter estimation degrade the accuracy of the model-based distance measure between a pixel and a cluster, which directly affects the performance of superpixel segmentation results. Further, the relative weight between statistical similarity and spatial proximity should be carefully selected to control the balance between boundary adherence and regularity of superpixels. An edge-dominated local clustering method is proposed to overcome these limitations. Edge information is introduced not only to define the dissimilarity of a pixel and a cluster but also to provide an adaptive grid with multiple layers for the initialization of cluster centers. Experiments on simulated and real datasets show that, compared with the previous algorithms using the statistical model-based dissimilarity, the proposed method produces superpixels, which have better edge adherence and stable performance.