Its been a long time but we used PCA in remote sensing to reduce the number of bands into a smaller subset that are easier to handle.
Satellite data is collected using sensors that are multispecteral/hyperspectral (for example LandSat has 11 bands, but sometimes there are over 100) but this can be cumbersome to work with. PCA can be applied to the data so that you have a smaller subset that contains most of the original information that makes further processing faster/easier
Sounds very cool. Howvever, when you transform the data using PCA the interpretation of the signals are different right? How do you approach that problem?
I see this is another way to look at it. I was asking about how to interpret the components themselves. Your link suggests converting the the coefficients of PCA regression back to coefficients for the original variables.
Satellite data is collected using sensors that are multispecteral/hyperspectral (for example LandSat has 11 bands, but sometimes there are over 100) but this can be cumbersome to work with. PCA can be applied to the data so that you have a smaller subset that contains most of the original information that makes further processing faster/easier