Based on the original, linear minimum noise fraction (MNF) transformation and kernel principal component analysis, a kernel version of the MNF transformation was recently introduced. Inspired by we here give a simple method for finding optimal parameters in a regularized version of kernel MNF analysis. We consider the model signal-to-noise ratio (SNR) as a function of the kernel parameters and the regularization parameter. In 2-4 steps of increasingly refined grid searches we find the parameters that maximize the model SNR. An example based on data from the DLR 3K camera system is given.
Ieee International Geoscience and Remote Sensing Symposium Proceedings, 2012, p. 370-373
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Ieee International Geoscience and Remote Sensing Symposium
IEEE International Geoscience and Remote Sensing SymposiumIEEE International Geoscience and Remote Sensing Symposium, 2012