Superpixel algorithms are aimed to partition an image into multiple similar sized segments based on similarity and proximity of pixels. In the heterogeneous regions, the boundaries of the objects should adhere well to the superpixels, and in the homogeneous parts, the pixels should be clustered so that compact superpixels are generated. Since speckle noise inherently exists in synthetic aperture radar (SAR) images their segmentation is considerably more difficult. In this article, the first contribution is the use of Mahalanobis distance instead of Euclidian, so that the superpixels can have elongated shapes to fit the complex structure of the real world better. Secondly, this geometric distance term is combined with similarity ratio term, which leads to even better performance on SAR images. Finally, the global constant that determines the relative importance of geometric proximity and pixel intensity similarity terms, whose best value should be chosen for each image, is considered. Instead of a global constant, its value is determined individually for each superpixel pair as a function of the average values of the superpixels. Experimental results with synthetic and real images demonstrate that the proposed approach has better segmentation performance than many of the existing state-of-the-art algorithms.