Sandia National Laboratories produces copious amounts of high-resolution, single-polarization Synthetic Aperture Radar (SAR) imagery, much more than available researchers and analysts can examine. Automating the recognition of terrains and structures in SAR imagery is highly desired. The optical image processing community has shown that superpixel segmentation (SPS) algorithms divide an image into small compact regions of similar intensity. Applying these SPS algorithms to optical images can reduce image complexity, enhance statistical characterization and improve segmentation and categorization of scene objects. SPS algorithms typically require high SNR (signal-to-noise-ratio) images to define segment boundaries accurately. Unfortunately, SAR imagery contains speckle, a product of coherent image formation, which complicates the extraction of superpixel segments and could preclude their use. Some researchers have developed modified SPS algorithms that discount speckle for application to SAR imagery. We apply two widely-used SPS algorithms to speckle-reduced SAR image products, both single SAR products and combinations of multiple SAR products, which include both single polarization and multi-polarization SAR images. To evaluate the quality of resulting superpixels, we compute research-standard segmentation quality measures on the match between superpixels and hand-labeled ground-truth, as well as statistical characterization of the radar-cross-section within each superpixel. Results of this quality analysis determine the best input/algorithm/parameter set for SAR imagery. Simple Linear Iterative Clustering provides faster computation time, superpixels that conform to scene-relevant structures, direct control of average superpixel size and more uniform superpixel sizes for improved statistical estimation which will facilitate subsequent terrain/structure categorization and segmentation into scene-relevant regions.