At the inception of this research, the interplay between super-resolution techniques and object detection frameworks remained largely unexplored, particularly in the context of satellite or overhead imagery. Intuitively, super-resolution methods should increase object detection performance, as an increase in resolution should add more distinguishable features that an object detection algorithm can use for discrimination.

This trade study strived to answer these foundational questions:

• Does the application of a super-resolution (SR) technique affect the ability to detect small objects in satellite imagery?
• Across what resolutions are these SR techniques effective?
• What is an ideal or minimum viable resolution for object detection?
• Can one artificially double or even quadruple the native resolution of coarser imagery to make the data more useful and increase the ability to detect fine objects?

Our results showed that the application of SR techniques as a pre-processing step provided a statistically significant improvement in object detection performance in the finest resolutions.  For both object detection frameworks, the greatest benefit is achieved at the highest resolutions, as super-resolving native 30 cm imagery to 15 cm yields a 13−36% improvement in mean average precision. Furthermore, when using YOLT, we find that enhancing imagery from 60 cm to 15 cm provides a significant boost in performance over both the native 30 cm imagery (+13%) and native 60 cm imagery (+20%).