If you are talking about the normalization option that you can find here: https://sarproz.com/manual/change_detect.html (sorry, I noticed we did not include yet a module description in the manual -but you can find several tutorials on it…-) the idea is the following:
change = filter [ (A1 – A2)/(A1 + A2) ]
The normalization allows detecting any changes, not only those affecting high amplitude values. What is actually leading to the detection of a change is the spatial correlation (implemented via a wiener filter).
This concept is based on having 2 images. If you have only 2 images, you can find signals only by looking at a spatial correlation.
If on the contrary you have many images, you do not need to focus on spatial statistics. It means, you can extract 1 single pixel and observe the pattern in time.
The automatic analysis of amplitude time series is implemented here: https://sarproz.com/manual/amp_series_proc.html
And here you find an example/exercise: https://www.sarproz.com/publish/tutorials_partIII.pdf
If you want to visualize a combination of spatial/temporal statistics by selecting pixels one by one, use this: https://sarproz.com/manual/class_sito.html
We don’t have yet an exercise showing how to use it, but we can show you one day if you’d like.