Parameter optimization in the regularized kernel minimum noise fraction transformation

Allan Aasbjerg Nielsen, Jacob Schack Vestergaard

AbstractThis contribution gives a simple method for finding optimal parameters in a regularized version of the recently suggested kernel minimum noise fraction (kMNF) transformation. The method considers the model signal-to-noise ratio (SNR) as a function of the kernel parameter(s) and the regularization parameter. In a grid search we find the parameters that maximize the model SNR. The method is succesfully demonstrated on a remote sensing change detection example with data from the DLR 3K camera system covering a busy motorway. In this example no regularization is chosen and the optimized choice for the kernel parameter gives much better SNR than the default choice.
TypeConference paper [With referee]
Year2012    Month July    pp. 370-373
AddressMunich, Germany
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Image Analysis & Computer Graphics, Geoinformatics