Leveraging Deep Learning for High-Resolution Optical Satellite Imagery From Low-Cost Small Satellite Platforms

Abstract

The expansion of small satellite networks in earth's orbit has resulted in a plethora of earth optical imagery available to the civil, defense, and commercial sectors. Small satellites (less than 1000 kg in mass) and their constellations can be delivered rapidly and at low cost and are more difficult to target by adversaries—a key consideration in the defense industry. Yet, small satellite size constraints often result in reduced payload capacity, reduced power capacity, or loss of redundancy. Traditionally, the cost of an optical telescope on board a satellite scales at roughly the square of the aperture, meaning that it costs four times as much to double the resolution of the imaging hardware. However, deep learning has shown considerable success in the areas of super-resolution and enhancing the pixel resolution of optical imagery. These deep learning methods have the potential to provide optical resolution capabilities rivaling larger satellites and their telescopes, while maintaining the benefits of small satellites—smaller physical size (which lowers launch vehicle costs and provides a basis for large constellations), reduced manufacturing time, and lower manufacturing costs. By providing low-cost small satellite platforms with the same capabilities as larger satellites, the cost for high-resolution in-orbit optical imagery is reduced alongside time to orbit. In this work, we detail a deep-learning-based approach, which improves optical satellite imagery to five times the original pixel-based resolution without the need or expense of increasing the capabilities of the imager through larger telescope apertures. The approach—demonstrated on Terran Orbital's GEOStare SV2 mission imagery—is generally applicable to any optical satellite image and is agnostic to the mission, satellite manufacturer, optical payload specifications, or data source. This capability provides a basis for small satellite missions and constellations—and their optical payloads—to rival the native hardware-based resolutions available through larger satellites with wider telescope apertures at a significantly reduced cost.

Keywords

• Deep learning
• deep neural networks (DNNs)
• optical imagery
• remote sensing
• satellite
• super-resolution (SR)