Simulation in Remote Sensing

Remote sensing images are expensive to buy. Remote sensing sensors are very, very expensive to design and build. Therefore, it may be interesting to know, before investing any money in images or sensors, which are the capabilities of an existing or future sensor.
In the Spring of 2009, Germain Forestier was a visiting scientist at CNES and we worked on this subject.
We (well, it was actually him who did the work!) implemented a simple simulator which used several spectral data bases, a set of sensors’ spectral responses and generated as output the spectra which would have been obtained for each material of the database by each of the sensors.
The, we just applied classification algorithms in order to assess the quality of the classification results for each sensor. This simulator did not integrate atmospheric effects or spatial resolution information, so the conclusions drawn can not be used as general truth. However, we could show interesting things such as for instance, that the better results obtained by Pleiades HR with respect to Quickbird are due to the different design of the Near Infra-Red band (full disclosure: I work at CNES, where Pleiades was designed).
The detailed results of this work were published last Summer: G. Forestier, J. Inglada, C. Wemmert, P. Gancarski, Mining spectral libraries to study sensors’ discrimination ability, SPIE Europe Remote Sensing, Vol. 7478, 9 pages, Berlin, Germany, September 2009
After Germain’s leave at CNES, we have continued to work a little bit on the simulator in order to make it more realistic. We have already included atmospheric effects and plan to go further by introducing spatial resolution simulation.
I am convinced that this is the way to go in order to cheaply assess the characteristics of a given sensor in terms of end-user needs and not only in terms of system quality issues as for instance SNR.