Epic for this feature: Jira Legacy
| Jira Legacy | ||||||
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Run the TopsStack Processor (PGE) using the link here. A merged directory, similar to the one shown below, will be used as the input. It must contain the baselines, geom_master, and SLC sub-directories.
Output Structure of Merged Directory
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Anaconda (with isce2 and mesa-libgl-cos6-x86_64)
StaMPS
ISCE
Triangle
Need to compile triangle with following command:
cc -O -o triangle triangle.c -lm
Snaphu (v2.0.0)
For MacOS, compile the snaphu executable from the src directory using
makecommand
MATLAB
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mt_prep_isce 0.4 3 2 50 200 where
0.4 = amplitude dispersion (0.4-0.42 are reasonable values)
3 = number of patches in range (default 1)
2 = number of patches in azimuth, (default 1)
50 = overlapping pixels between patches in range (default 50)
200 = overlapping pixels between patches in azimuth (default 200)
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| Code Block |
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INSAR_20190506
├── 20190518
├── 20190530
├── PATCH_1
├── PATCH_2
├── PATCH_3
├── PATCH_4
├── PATCH_5
├── PATCH_6
├── STAMPS.log
├── baselineGRID_20190518
├── baselineGRID_20190518.vrt
├── baselineGRID_20190518.xml
├── baselineGRID_20190530
├── baselineGRID_20190530.vrt
├── baselineGRID_20190530.xml
├── baseline_overview_new
├── bperp.1.in
├── calamp.in
├── calamp.out
├── day.1.in
├── dem.raw
├── dem.raw.vrt
├── dem.raw.xml
├── e.raw
├── e.raw.vrt
├── e.raw.xml
├── heading.1.in
├── heading.raw
├── heading.raw.vrt
├── heading.raw.xml
├── inc_angle.raw
├── inc_angle.raw.vrt
├── inc_angle.raw.xml
├── input_file
├── isce.log
├── lambda.1.in
├── lat.raw
├── lat.raw.vrt
├── lat.raw.xml
├── len.txt
├── lon.raw
├── lon.raw.vrt
├── lon.raw.xml
├── master
├── master_day.1.in
├── n.raw
├── n.raw.vrt
├── n.raw.xml
├── parms.mat
├── patch.list
├── processing_SM.log
├── processor.txt
├── ps_parms_initial.log
├── pscdem.in
├── psclonlat.in
├── pscphase.in
├── selpsc.in
├── slc_osfactor.1.in
├── slcs.list
├── u.raw
├── u.raw.vrt
├── u.raw.xml
└── width.txt
9 directories, 54 files |
2. In the terminal, launch MATLAB by typing in matlab. This will open the MATLAB program with the INSAR_master_date directory.
The parameters that control the processing are set to default values which you can view with:
>>getparm
You can modify any parameters from the default using
>>setparm(‘param_name’,param_value)
Only enough characters of param name to make it unique are required. Setting param value to nan resets the parameter to the default value.
To run stamps end to end, use the following command:
>>stamps
This will run the following steps:
Step 1: Load data
Step 2: Estimate phase noise
Step 3: PS selection
Step 4: PS weeding
Step 5: Phase correction
Step 6: Phase unwrapping
Step 7: Estimate spatially-correlated look angle error
Step 8: Atmospheric filtering
The default is to run all steps. A subset of steps can also be selected, see >>help stamps for details.
If needed, there are more details about PS Processing using StaMPS in the StaMPS_Manual.pdf below:
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Time Series Plots
Example 1:
>>ps_plot('v-d',1,0,0,[1:3,7:8],'ts')
Plots the mean LOS velocity, calculated from single master interferograms for PS processing, or small baseline interferograms for SB and combined processing. The ‘ts’ switch enables the user to select individual points for generating time series (TS) plots.
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You can change ‘radius factor’ in the white text box to search in a larger radius, unless you can’t find points with the default factor (search radius = radius factor * radius, where radius is 1 arcsecond by default). After selecting a point, the following plots are produced:
Figure 2 is a v-d graph centered on the point selected with a radius around that point
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Figure 3 is a LOS-time graph based on the points within the circular area in Figure 2
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Example 2:
>>ps_plot('v-d',1,0,0,[], 0, 0, 0,[], [28.6 28.9], [41.2 41.27],'ts')
plots velocities for the subset defined by longitude and latitude, and generates TS plots, the rest of the switches are set to default values.
Tip: The position of ‘ts’ switch could be anywhere after plot option ‘v-d‘. For details type ps plot without any arguments on the matlab prompt to get help.
Using the ‘V-D’ option (rather than ‘v-d’) forces the use of single master interferograms for the estimation, even for SB or combined processing
can’t find points with the default factor (search radius = radius factor * radius, where radius is 1 arcsecond by default). After selecting a point, the following plots are produced:
Figure 2 is a v-d graph centered on the point selected with a radius around that point
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Figure 3 is a LOS-time graph based on the points within the circular area in Figure 2
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Testing: Google earth KML file
Example 1:
>>ps_gescatter('project_velo.kml',ph_disp,10,0.4)
Using ps_gescatter.m will generates a kml file from ph_disp matrix for every 10 points with an opacity of 0.4. Later, you can load the ‘project_velo.kml’ file to google earth for visualization.
Tip: Use >>ps_plot('v-d',-1) to save PS velocity estimation to a mat file and later retrieve with >>load_ps_plot_v-d ph_disp command and use as an input to ps_gescatter.m .
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