Reconstructions from Compressive 2 Random Projections of Hyperspectral 3 Imagery 4

7 processing. Conventional dimensionality reduction on-board remote devices is 8 often prohibitive due to limited computational resources; on the other hand, 9 integrating random projections directly into signal acquisition offers an alternative 10 to explicit dimensionality reduction without incurring sender-side computational 11 cost. Receiver-side reconstruction of hyperspectral data from such random pro12 jections in the form of compressive-projection principal component analysis 13 (CPPCA) as well as compressed sensing (CS) is investigated. Specifically con14 sidered are single-task CS algorithms which reconstruct each hyperspectral pixel