Parameter optimization in the regularized kernel minimum noise fraction transformation |
Allan Aasbjerg Nielsen, Jacob Schack Vestergaard
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| Abstract | This 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. |
| Type | Conference paper [With referee] |
| Conference | IEEE IGARSS |
| Year | 2012 Month July pp. 370-373 |
| Address | Munich, Germany |
| Electronic version(s) | [pdf] |
| BibTeX data | [bibtex] |
| IMM Group(s) | Image Analysis & Computer Graphics, Geoinformatics |