Onboard deep learning for efficient small satellite reflectance retrievals and downlink

Abstract

The rapid pace of commercial space launches has drastically increased opportunities for novel small satellite missions. Though small satellites benefit from reduced launch costs, they are constrained by their Size, Weight, and Power (SWaP) limitations. The satellites in MyRadar’s HORIS (Hyperspectral Orbital Remote Imaging Spectrometer) constellation have SWaP constraints of a 1U CubeSat form factor (10 cm3 and ⪅ 1.33 kg), which place significant limits on mission design and duty cycle. In particular, downlinking large amounts of raw data, like that generated by the HORIS narrow band hyperspectral sensor, can be prohibitively bandwidth and power intensive. This study explores using deep learning inference to optimize onboard data processing and to mitigate the impacts of data volume on downlinking hyperspectral information from LEO (Low Earth Orbit). In particular, deep learning inference on the visible wavelength context imagery is used to constrain the aerosol model effects on reflectance retrieval calculations to convert at-sensor radiance measurements into surface (or cloud top) reflectance estimates. Also, a transfer learning approach utilizing an adversarially trained autoencoder to compress data from other satellites is used for HORIS data compression to reduce the required power and bandwidth for alerting use cases.