I’m processing some S1 GRD products using several GPT operators in sequence (the Apply Orbit step has already been run):
Calibration
Speckle Filter
Terrain Correction
These are run using three gpt calls. Initially the combined processing time was around 15 minutes. However, when I decide to repeat the processing this time increases; if I keep repeating the processing it just increases further. I’ve encountered run times of 45 mins, 80 mins, up to nearly 3 hours. Each time I run the processing I am saving the DIM files in a new directory .
I haven’t yet been able to test this on another machine but was wondering if anyone else has come across the same behavior. Perhaps there’s something I’m missing?
Some times I faced up same problem, while using the same .xml, I think you could try to save the xml of new data in different name and place, might be help.
I created a graph with some custom parameters to fulfill my requirements. Running these processes as a graph (as opposed to separate operators) drastically changed the run time. In addition, repeated processing seems to take the same amount of time; I am now getting a run time of 6 minutes.
I hadn’t been aware of the extent to which you can use custom parameters in graphs. My main reason for going with three separate calls to the operators was to provide additional operator-specific arguments. I found the Rice detection with Sentinel-1 using SNAP GPT RUS Webinar and step forum post (credit to @marpet ) very helpful.
I’m still not sure why using the operators was resulting in the increasing processing times but I have enough of a resolution to progress so I will flag as solved.