Satellite-to-earth data transmissions are increasingly becoming a bottleneck, as transmission speed improvements do not keep up with the pace of on-board data generation. Hence, on-board satellite payload data processing becomes essential, provided such processing can be performed with a sufficiently small energy footprint. In this work we demonstrate that with appropriate pruning of weights, suitable data structures to reduce off-chip memory requirements, and a highly parallel application-specific architecture, Field Programmable Gate Array (FPGA) technology can be used for on-board satellite processing of observation by Convolutional Neural Network (CNN) architectures, and at an order-ofmagnitude smaller energy requirements compared to Graphics Processing Units (GPUs) running the same algorithms. We demonstrate a 0.4% error vs. results from Tensorflow running on GPUs towards estimation of the galaxy redshift from spectroscopic observations. The results are from actual executions on FPGAs which have space-qualified equivalent parts. The main contribution of this work is the demonstration that accurate observation analysis task can be performed in space, so that only critical information is transmitted to ground stations instead of raw data.