watpet / kfts-insar

Kalman Filter Time series Analysis for InSAR

Kalman Filter for time series analysis of InSAR data

A library to iterativelly recover the phase evolution over time from interferograms (stored in HDF5 file)

v0.1.0 :

Documentation

https://manondls.github.io/KFTS-InSAR/

Dalaison & Jolivet (2020)

Assumptions

• Assuming python 3 is installed on your system.
• H5py installed with gcc8 and openmpi variants for multiprocessing (py36-h5py @2.10.0_1+gcc8+openmpi)
• Numpy installed with gcc8 and openblas variants for multiprocessing (py36-numpy @1.18.1_0+gcc8+openblas)

Install kf on your system using :

python -m pip install dist/KFTS_InSAR-0.1-py3-none-any.whl

Easy test

A sample dataset (set of unwrapped interferograms) is provided in testdata/ To run KFTS on this dataset :

python kfts.py -c configs/config_Etna.ini 

To quickly visualize outputs and plot metrics :

python kf2rms.py -c configs/config_Etna.ini 
python checkinnov.py -c configs/config_Etna.ini 
python plotoutput.py -c configs/config_Etna.ini  -geom ./ -rmsTh 2

Synthetics

To reproduce the synthetic data set in Dalaison & Jolivet (2020)

python synthetic_data.py

Prepare input H5file

A working script for ISCE is provided. If other software are used, adjustment of formats and file structure probably has to be done. Parameters in config file must be adjusted to your setting.

python prepare_input.py -c configs/config_prepareinput.ini 

Many options are not required by KFTS but help clean the interferogram stack. Examples include deramping, filtering out low coherence region, producing longitude and latitude files with the same geometry as interferograms, cutting interferogram edges. Referencing to a common region defined in all interferograms (not NaN) is compulsory. Zeros in interferograms are assumed to be NaNs.