  Recent advances in signal processing have focused on the use of sparse
representations in various applications. A new field of interest based on
sparsity has recently emerged: compressed sensing. This theory is a new
sampling framework that provides an alternative to the well-known Shannon
sampling theory. In this paper we investigate how compressed sensing (CS) can
provide new insights into astronomical data compression and more generally how
it paves the way for new conceptions in astronomical remote sensing. We first
give a brief overview of the compressed sensing theory which provides very
simple coding process with low computational cost, thus favoring its use for
real-time applications often found on board space mission. We introduce a
practical and effective recovery algorithm for decoding compressed data. In
astronomy, physical prior information is often crucial for devising effective
signal processing methods. We particularly point out that a CS-based
compression scheme is flexible enough to account for such information. In this
context, compressed sensing is a new framework in which data acquisition and
data processing are merged. We show also that CS provides a new fantastic way
to handle multiple observations of the same field view, allowing us to recover
information at very low signal-to-noise ratio, which is impossible with
standard compression methods. This CS data fusion concept could lead to an
elegant and effective way to solve the problem ESA is faced with, for the
transmission to the earth of the data collected by PACS, one of the instruments
on board the Herschel spacecraft which will be launched in 2008.
