A Feasibility Study of On-Board Cloud Detection and Compression

On-board image classification has the ability to significantly impact the design of future exploration missions. Classification algorithms on Earth observing satellites could be used to point the satellite at dynamic natural phenomena (such as erupting volcanoes), tag high priority data for expedited analysis on the ground, or trigger increased compression in lower priority scenes. For high data volume Earthobserving missions utilizing a spectrometer in the visible, short-wave and infrared wavelengths, it may be acceptable to lossily compress pixels containing clouds to reduce the data downlink volume. This study will evaluate the feasibility and advantage of using an on-board cloud detection and compression algorithm. The accuracy of an algorithm will be discussed, the resulting data volume savings will be calculated and the performance of a sample algorithm on an FPGA will be characterized. The detection algorithm will be tested on sample data from a similar airborne spectrometer. It is desired that the detection algorithm minimizes the incidence of false positive cloud detection. The suggested compression involves reducing the radiometric and spectral resolution of the cloudy pixels. Providing the capability for autonomous image classification on an Earth-observing mission opens the door for more extensive classification in later mission stages and flexibility to changing mission requirements.