With trends moving to not only process data in orbit but to analyse it as well, machine learning can be instantiated for edge inference in space. This FPGA based solution is the first of its kind. The task of processing raw sensor information is very intensive and requires rigorous signal processing techniques such as non-uniformity correction, decimation, equalisation, pulse compression, beamforming and a variety of others. The task of then rendering usable images can include additional tasks such as rotation, back-projection and ortho-rectification. Not all these tasks have traditionally been possible on-orbit, but the KU060 opens the door to making this possible.
Cloud detection is a relatively straightforward problem – 66% of earth is covered in clouds at any given time. If the images are being captured and stored, most of them are of clouds. When the processing is done, these images are stored and sent down to earth, which is a waste of bandwidth and storage - two very valuable resources for a spacecraft. Now, if a machine learning algorithm was implemented to discard images of cloud and only store and transmit information related to everything but clouds, critical bandwidth and storage are saved! Neural networks is one such technique.