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. In a previous study1 we gave new insights into the use of Compressed Sensing (CS)
in the scope of astronomical data analysis. More specifically, we showed how CS is flexible enough to
account for particular observational strategies such as raster scans. This kind of CS data fusion concept
led 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 onboard the Herschel spacecraft which will
launched in late 2008/early 2009.
In this paper, we extend this work by showing how CS can be effectively used to jointly decode multiple
observations at the level of map making. This allows us to directly estimate large areas of the sky
from one or several raster scans. Beyond the particular but important Herschel example, we strongly
believe that CS can be applied to a wider range of applications such as in earth science and remote
sensing where dealing with multiple redundant observations is common place. Simple but illustrative
examples are given that show the effectiveness of CS when decoding is made from multiple redundant