As we know, SNAP give a selection about removing thermal noise from Sentinel-1 imagery. However, I don’t know whether noise removal is a must by ourselves after a step in SNAP S-1 Thermal Noise Removal .
I think the results still have serious thermal contaminate after a thermal noise removal in SNAP, as following example, I have done removal but the noise is obvious.
can you please give use the full name of the product?
I don’t have a solution but I would like to try it myself.
The image name is S1A_EW_GRDM_1SDH_20170130T154345_20170130T154445_015063_0189DC_2AFA .
But, I need to say the image thermal noise removal is using Python code( https://git.earthdata.nasa.gov/projects/ASR/repos/asf-daac-script-recipes/browse/procSentinelRTC_recipe_withLicenseInfo.py). In this code, it uses thermal noise removal in SNAP module. Therefore, I regard it as processed using SNAP in my question. Sorry.
The above image has also been converted to dB value.
I’ve just tried with snap.
Top : before thermal removal
Bottom : After thermal removal
However, the preview image of the product shows these stripes
Sorry, the image shown before is HV-pol image rather than HH-pol one. Banding effect noise is not obvious in HH-pol, this phenomenon is a problem for HV-pol images . Some papers about sea ice classification will only do incidence correction for HH-pol and thermal noise removal for HV-pol imagery.
My bad. And you’re right. If I look at HV, the stripes are huge, before AND after thermal noise removal
We can however see a difference. The last four are now ok, with the first one still much brighter
Do you know the reason of this effect? I’m interested
Yes, you are right. But the picture I given is dB value. In dB image, the phenomenon is more obvious than your result. But I don’t know the reason for your question.Sorry.
Even converted in db, we can still see the positive effect of thermal noise removal. But yes it’s far from perfect
The paper(https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8126233) maybe useful to find the reason of the stripes.
Great. It looks like Snap calibrates the data using ESA noise vectors. That is confirmed in the lines of codes of snap : https://github.com/senbox-org/s1tbx/blob/31fb1eceefdb4b2ff3878f8c35649029fa4ae322/s1tbx-op-calibration/src/main/java/org/esa/s1tbx/calibration/gpf/Sentinel1RemoveThermalNoiseOp.java
The corrections from the article are quite impressive.
I hope you will find a solution to your problem
Did you just figure out how to solve these corrections using snap in a cross polarized (HV) channel???
If yes, can you please elaborate on how you went ahead
I have same problem too, Did you just figure out how to solve these corrections using snap in a cross for HV? Let me know too
That quite some time ago. I have no idea sorry.