So if I understand well, the algorithm will try to separate the 19 bands in 4 (equal or unequal?) new clusters, but this time, only by looking at the LDA obtained for those bands? Also, for each cluster, you get 4 values, which correspond the the LDA of those bands.
- Just to be sure : the clusters only regroup connected (neighbor) bands?
yes, sdbands clustering defines groups of adjacent wavelengths.
- What is the criteria used to choose those 4 new clusters?
k-means clustering considering similarity between wavelengths
- Can a cluster include much more bands than another?
- Is there a possibility to see to which initial bands a cluster corresponds?
yes, sdbands(p) - check out the documentation: http://perclass.com/doc/guide/feature_extraction/spectra.html#ex:print
...and finally, as LDA transformation is already a linear combination of all bands, in which case do you think there could be a benefit to use this instead of directly using sdlda() ? When you need to emphasize local strategic variations of the reflectance?
computing LDA projection only for specific set of wavelengths effectively combines (feature) selection with LDA extraction. If you use LDA on complete spectra, each input is a linear combination of all wavelengths. Using LDA only within the bands makes each LDA simpler (i.e. less suffering from over-fitting if you only have a limited number of samples to learn from)
In my opinion, for some problems only certain parts of spectra are informative to separate classes of interest. Others are not and including them is just not necessary.
Also, after what I saw in different tests, the last (4th) LDA feature of each cluster doesn’t seem to contain any valuable information. Would it be wiser not to consider it during the classification?
Depends :-), To answer this, you can compare classifier results when using all LDA-extracted bands or when running a feature selection in the final feature space and using only a subset. Depending on your available sample size, this additional optimization step that requires further data splitting, may be too much to do robustly.
I hope this is clear… Sorry if I ask too many questions :-)
No problem, Good Luck!