  We explore the application of super-resolution techniques to satellite
imagery, and the effects of these techniques on object detection algorithm
performance. Specifically, we enhance satellite imagery beyond its native
resolution, and test if we can identify various types of vehicles, planes, and
boats with greater accuracy than native resolution. Using the Very Deep
Super-Resolution (VDSR) framework and a custom Random Forest Super-Resolution
(RFSR) framework we generate enhancement levels of 2x, 4x, and 8x over five
distinct resolutions ranging from 30 cm to 4.8 meters. Using both native and
super-resolved data, we then train several custom detection models using the
SIMRDWN object detection framework. SIMRDWN combines a number of popular object
detection algorithms (e.g. SSD, YOLO) into a unified framework that is designed
to rapidly detect objects in large satellite images. This approach allows us to
quantify the effects of super-resolution techniques on object detection
performance across multiple classes and resolutions. We also quantify the
performance of object detection as a function of native resolution and object
pixel size. For our test set we note that performance degrades from mean
average precision (mAP) = 0.53 at 30 cm resolution, down to mAP = 0.11 at 4.8 m
resolution. Super-resolving native 30 cm imagery to 15 cm yields the greatest
benefit; a 13-36% improvement in mAP. Super-resolution is less beneficial at
coarser resolutions, though still provides a small improvement in performance.
