We describe the development and implementation of plume detection algorithms under severe bandwidth and processing constraints imposed by a CubeSat architecture. In particular, two ideas will be presented: one employs onboard processing to reduce the data that is downlinked, and one employs the Sparse Matrix Transform (SMT) to speed up the onboard computation of an approximate Mahalanobis distance.
James Theiler, Bernard R. Foy, Claira Safi, and Steven P. Love, "Onboard CubeSat data processing for hyperspectral detection of chemical plumes," Proc. SPIE 10644, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, 1064405 (Presented at SPIE Defense + Security: April 17, 2018; Published: 8 May 2018); https://doi.org/10.1117/12.2305278.
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