Parameter optimization in the regularized kernel minimum noise fraction transformation
Publication: Research - peer-review › Article in proceedings – Annual report year: 2012
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.
| Original language | English |
|---|---|
| Title | IEEE International Geoscience and Remote Sensing Symposium proceedings |
| Publisher | IEEE |
| Publication date | 2012 |
| Pages | 370-373 |
| ISBN (print) | 978-1-4673-1160-1 |
| ISBN (electronic) | 978-1-4673-1158-8 |
| DOIs | |
| State | Published |
Conference
| Conference | IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2012) |
|---|---|
| Number | 2012 |
| Country | Germany |
| City | Münich |
| Period | 22-07-12 → 27-07-12 |
| Internet address | http://www.igarss2012.org/ |
| Name | IEEE International Geoscience and Remote Sensing Symposium |
|---|---|
| ISSN (Print) | 2153-6996 |
| Citations | Web of Science® Times Cited: No match on DOI |
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