A new frontier in the commercialization of space is emerging that offers lower-cost access to space-based remote sensing with companies. While capabilities vary across current and future providers of spaced-based imagery, we'll investigate how the application of modern imagery analysis techniques increase the complementing value of multiple remote sensing solutions. We architect and train deep convolutional neural networks to enhance lower resolution imagery from higher resolution imagery: super-resolution. During the super-resolution process, the peak signal-to-noise ratio (PSNR) is not uniform through out the image. To assist the imagery analyst, it is preferable to maximize PSNR gain in areas of interest. We investigate the distribution of PSNR gain during the super-resolution of a satellite image. We compare the results of PSNR with accuracy of object detection algorithms to measure the impact of super-resolution on standard computer vision problems.