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As part of the NEUROSAT program we have developed a MATLAB toolbox for processing scatterometer observations, SCATLAB. Additional funding for the software development on ambiguity removal was provided from EPSRC grant GR/L 03088, Combining Spatially-Distributed Predictions from Neural Networks.
SCATLAB is a collection of MATLAB functions written by Dan Cornford, David Evans and Guillaume Ramage during the course of the NEUROSAT project, at the Neural Computing Research Group, Aston University. The software is made freely available for those who are interested, but with no warantee, the software being covered by the GNU freeware licence. The software would be of interest to people familiar with scatterometer data, particularly for scatterometer wind field retrieval. However, there are many general routines, for instance for finding the Bayesian maximum a posteriori parameter values for a vector Gaussian random field, which may be of more general interest. For users less familiar with MATLAB there are two Graphical User Interface based demonstrations.
Wherever possible the functions have been properly documented, however the code is supplied as is. Further help can be obtained from the authors, however they are busy people (who isn't!) so don't expect too much! We hope that this suite of programs will grow with time and we welcome contributions and suggestions of enhancements. Please do not pass the software on as your own or sell it!
In order to run SCATLAB you will also need the NETLAB package, a neural network tool-box for MATLAB written here at the Neural Computing Research Group. For full functionality you will need version 2.0 of NETLAB which is not yet publicly available.
The software can be down-loaded here: scatlab.zip. You will need a utility to unzip the package and detailed instructions for installation and some rather basic documentation can be found in SCATLAB: NEUROSAT Software and Data Structure Conventions as gzipped postscript.