Mutli-temporal vs Single-date speckle filters difference


I’m trying to understand the difference between multi-temporal and single-date speckle filters that are implemented in SNAP.
As I understand it, the multi-temporal filter applies a weighted average of the selected spatial filter across all images of a time series. In this respect I would highly appreciate it if somebody could clarify:

  1. If this weighting done per pixel? If so, how come the output of the boxcar (mean) multi-temporal filter with 15x15 kernel looks much sharper than the one processed using the same single-date filter (see image below)?
  2. How the weights are calculated? I tried to dig into the Java code (see:, but I still don’t really understand what calculations are done in lines 363 and 374.

Thanks for any advice!

Did you apply the filter at power or dB scaled data?

See this post for details:

Usually dB-conversion should be done only at the end.