It is a method used to created an interferometric time series developed by Hooper [2004-2007] following the persistent scatterer approach of Ferretti 2001, which produces surface line-of-sight deformation with respect to the multi-temporal radar repeat-passes and mitgate the temporal phase decorrelations due to the instrument errors, DEM error and atmospheric contribution to the phase delay.

SNAP2StaMPS is a Python workflow developed by José Manuel Delgado Blasco and Michael Foumelis collaboration with Prof. A. Hooper to automate the pre-processing of Sentinel-1 SLC data and their preparation for ingestion to StaMPS. Much appreciation goes to the great work of those honorable professors.

StaMPS method follows 8 steps that are carried out on Matlab on a virtual Unix machine or a Linus OS system

If I understood this correctly. Step 3 selects separate groups (each subset contains a pre-determined density of pixels per square kilometer, those pixels have random phase) from the initially selected PS pixels in step 2 that was based on their calculated spatially correlated phase, spatially uncorrelated DEM error that is subtracted from the remaining phase, temporal coherence.

Then, in step 4 (weeding) those groups of pixels per unit kilometers are further filtered and oversampled and in each group, a selected pixel with highest SNR is taken as a reference pixel and the noise for the neighbouring adjacent pixels is calculated, then based on a pre-determined value of (‘weed_standard_deviation’), some of those neighbouring pixels are dropped and others are kept as PS pixels.

A) Am I correct?

B) What is a pixel with random phase?

C) What is the pixel noise? is it related to having multiple elementary scatterers where none of them is dominant therefore their backscattererd signal is recieved at a different collective phase at each aquision even if the ground containing those scatterers were stable over time ?

D) Due to the language barrier, I have read Hooper's 2007 paper Article Persistent scatterer InSAR for crustal deformation analysis,...

E) what is difference between by the spatially uncorrelated DEM (look angle) error that is filtered at StaMPS step 3, removed at Step 5, and spatially correlated look angle error that is removed at Stamps Step 7?

There are attached some test results and I would appreciate if someone inform me how I may remove the persistent atmospheric contribution. I have only used the basic APS linear approach using TRAIN toolbox developed by Bekaert 2015