fringe2mintpy

introduction

fringe (fine resolution insar using gerenalized eigenvector) is developed by NASA JPL to do the PSDS estimation. https://github.com/isce-framework/fringe

Since there's no clear instruction for fringe to do the sbas timeseries estimation, here I write a simple bridge code converting fringe result to another sbas code, Mintpy.

Mintpy is a small baselines timesereis code developed by Yunjun Zhang at Miami University. https://github.com/insarlab/MintPy

Step by step

data preparation

Using ISCE stack tools (stackSentinel.py, stackStripmap.py) in SLC mode to prepare the coregistrated stack. *ISCE is another code deeloped by NASA JPL to preprocess InSAR/D-InSAR.

fringe

Following the instruction from fringe to run the PSDS estimator. https://github.com/isce-framework/fringe/blob/main/docs/workflows.md

integratePS

In fringe workflow, instead of running script in the original repo, I modified the script to export SLC stack.

./integratePS.py -s coreg_stack/slcs_base.vrt -d adjusted_wrapped_DS \
                 -t Sequential/Datum_connection/EVD/tcorr.bin \
                 -p ampDispersion/ps_pixels 
                 -o PS_DS --stamps

GenerateIgram and run files

modified path parameter in generateIgram.py (or run by default)

./generateIgram.py and three run files, run_geneateIgram.sh, run_estCoherence.sh and run_unwrap.sh is created.

Run them respectively.

mintpy

assign the path of unwrap image, coherence, connect component to the path we just genreate in smallbaselineApp.cfg. Run smallbaselineApp.py smallbaselineApp.cfg

FRInGE SBAS mode

Here I made some modification to the sbas code to Network.py for ISCE baseline input.

./integratePS.py -s coreg_stack/slcs_base.vrt -d adjusted_wrapped_DS/ \
                 -t Sequential/Datum_connection/EVD/tcorr.bin \
                 -p ampDispersion/ps_pixels -b ../baselines \
                 -o PS_DS --sbas -u snaphu

But the workfllow is not yet tested.

contact info

email: [email protected]**