My problem is that I can’t delete the speckle of the images. Oddly, with the coregistration I’m having worse results.
With these results I pretend to calculate the velocity and acceleration of the formation of slums, without coregistration the results can slightly identify this formation, but with a lot of accelerations in places where there hasn’t, the results with the coregistration appears to be accelerations everywhere.
I’m starting to think that there’s the presence of white noise. Anyone had ever have a similar problem? Or have any idea of how to solve it?
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The code of my images are: S1B_IW_GRDH_1SDV_20200129T100757_20200129T100822_020030_025E5E_350E S1B_IW_GRDH_1SDV_20200222T100756_20200222T100821_020380_0269AD_7339 S1B_IW_GRDH_1SDV_20200329T100756_20200329T100821_020905_027A54_42E6 S1B_IW_GRDH_1SDV_20200422T100757_20200422T100822_021255_02856A_41B0 S1B_IW_GRDH_1SDV_20200528T100759_20200528T100824_021780_029573_34F6 S1B_IW_GRDH_1SDV_20200621T100801_20200621T100826_022130_02A004_E513 S1B_IW_GRDH_1SDV_20200727T100803_20200727T100828_022655_02B005_5CCA S1B_IW_GRDH_1SDV_20200820T100804_20200820T100829_023005_02BAC2_68AA S1B_IW_GRDH_1SDV_20200925T100806_20200925T100831_023530_02CB37_439F S1B_IW_GRDH_1SDV_20201031T100806_20201031T100831_024055_02DB99_EA78 S1B_IW_GRDH_1SDV_20201124T100805_20201124T100830_024405_02E692_0237 S1B_IW_GRDH_1SDV_20201230T100804_20201230T100829_024930_02F774_42A7 AOI: POLYGON ((-70.1821194355951 -20.1625259834406,-70.0374695163088 -20.1651619728581,-70.0401055057263 -20.3068464040496,-70.1831079316267 -20.3042104146321,-70.1821194355951 -20.1625259834406))
I’m not sure if this could be the problem, but the Lee filter is made to preserve edges in images, which means that it doesn’t remove as much speckle where there are lots of small objects in an area. I am not sure if this is the root of the issue here, but it could be worth trying to use a simple boxcar filter instead of the refined Lee, and see if it gives better results.
Thanks for your answer Paul! I change the filter to boxcar (3x3), but my problem still occuring. Once that I applied the serie (Thermal Noise Removal > Orbit file > Calibration > Range Doppler Terrain correction (Alos Palsar)> Stack > Multi-temporal speckle filter(Boxcar)) I calculated the acceleration of change (SNAP, band math) (for example, (March -2*February + January /2)) my results look like this:
The calculation on snap results accelerations everywhere. The calcule with separate bands looks better, but with a lot of noise in places where there hasn’t have any changes. I don’t know if this is a problem of the speckle filter or the calculation. Anyone has ever have a similar problem with this? I will appreciate some help! Thanks
I can see that in the series of transformations you indicate, you do not transform the data to dB before applying band maths using the formula you indicate, which would be required for additions and subtractions of the received intensities.
I think it could also help to see if there is an underlying issue to try to detect change between only two images, with for example March/February before transforming to dBs (or March-February for dB values).
B) Standar Workflow, using single speckle filter (Refined-lee filter) .
I been working on this and still can’t remove the noise. You suggest to apply the workflow until the terrain correction, make the band math (for accelerations and velocities) and then convert the values to dB?
Even if I pretend to make a time series images to evaluate the apparition of slums? There’s any filter in particular that you recomend? I’ve been using the Lee Sigma, Refined Lee and Boxcar and the problem with the noise still appearing.
You cannot really expect speckle to disappear completely without filtering heavily, which will degrade resolution one way or another. I don’t really know which single speckle filter to recommend but the Lee filters should be ok. Anyway, you should drop the multi-temporal filtering as it is made for situations where the structures stay stable for the whole period.
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