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Merge pull request #92 from yunjunz/stack_readme 

stack: convert README.txt to README.md
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To use the TOPS or Stripmap stack processors you need to:
1- Install ISCE as usual
2- Depending on which stack processor you need to try, add the path of the folder containing the python scripts to your $PATH environment variable as follows:
- to use the topsStack for processing a stack of Sentinel-1 tops data add the full path of your "contrib/stack/topsStack" to your $PATH environemnt variable
- to use the stripmapStack for processing a stack of Stripmap data, add the full path of your "contrib/stack/stripmapStack" to your $PATH environemnt variableu
NOTE:
The stack processors do not show up in the install directory of your isce software. They can be found in the isce source directory.
Important Note:
There might be conflicts between topsStack and stripmapStack scripts (due to comman names of different scripts). Therefore users MUST only have the path of one stack processor in their $PATH environment at a time, to avoid conflicts between the two stack processors.

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## Stack Processors
Read the document for each stack processor for details.
+ [stripmapStack](./stripmapStack/README.md)
+ [topsStack](./topsStack/README.md)
### Installation
To use the TOPS or Stripmap stack processors you need to:
1. Install ISCE as usual
2. Depending on which stack processor you need to try, add the path of the folder containing the python scripts to your `$PATH` environment variable as follows:
- add the full path of your **contrib/stack/topsStack** to `$PATH` to use the topsStack for processing a stack of Sentinel-1 TOPS data
- add the full path of your **contrib/stack/stripmapStack** to `$PATH` to use the stripmapStack for processing a stack of StripMap data
Note: The stack processors do not show up in the install directory of your isce software. They can be found in the isce source directory.
#### Important Note: ####
There might be conflicts between topsStack and stripmapStack scripts (due to comman names of different scripts). Therefore users **MUST only** have the path of **one stack processor in their $PATH environment at a time**, to avoid conflicts between the two stack processors.
### References
Users who use the stack processors may refer to the following literatures:
For StripMap stack processor and ionospheric phase estimation:
+ H. Fattahi, M. Simons, and P. Agram, "InSAR Time-Series Estimation of the Ionospheric Phase Delay: An Extension of the Split Range-Spectrum Technique", IEEE Trans. Geosci. Remote Sens., vol. 55, no. 10, 5984-5996, 2017. (https://ieeexplore.ieee.org/abstract/document/7987747/)
For TOPS stack processing:
+ H. Fattahi, P. Agram, and M. Simons, “A network-based enhanced spectral diversity approach for TOPS time-series analysis,” IEEE Trans. Geosci. Remote Sens., vol. 55, no. 2, pp. 777–786, Feb. 2017. (https://ieeexplore.ieee.org/abstract/document/7637021/)

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The detailed algorithms for stack processing of stripmap data can be found here:
H. Fattahi, M. Simons, and P. Agram, "InSAR Time-Series Estimation of the Ionospheric Phase Delay: An Extension of the Split Range-Spectrum Technique", IEEE Trans. Geosci. Remote Sens., vol. 55, no. 10, 5984-5996, 2017. (https://ieeexplore.ieee.org/abstract/document/7987747/)
-----------------------------------
Notes on stripmap stack processor:
Here are some notes to get started with processing stacks of stripmap data with ISCE.
1- create a folder somewhere for your project
mkdir MauleT111
cd MauleT111
2- create a DEM:
dem.py -a stitch -b -37 -31 -72 -69 -r -s 1 -c
3- Keep only ".dem.wgs84", ".dem.wgs84.vrt" and ".dem.wgs84.xml" and remove unnecessary files
4- fix the path of the file in the xml file of the DEM by using this command:
fixImageXml.py -f -i demLat_S37_S31_Lon_W072_W069.dem.wgs84
5- create a folder to download the ALOS-1 data from ASF:
mkdir download
cd download
6- Download the data that that you want to process to the downlowd directory.
7- once all data have been downloaded, we need to unzip them and move them to different folders and getting ready for unpacking and then SLC generation.
This can be done by running the following command in a directory above "download":
prepRawALOS.py -i download/ -o SLC
This command generates an empty SLC folder and a run file called: "run_unPackALOS".
You could also run prepRawSensor.py which aims to recognize the sensor data automatically followed by running the sensor specific preparation script. For now we include support for ALOS and CSK raw data, but it is trivial to expand and include other sensors as unpacking routines are already included in the distribution.
prepRawSensor.py -i download/ -o SLC
8- execute the commands inside run_unPackALOS file. If you have a cluster that you can submit jobs, you can submit each line of command to a processor. The commands are independent and can be run in parallel.
9- After successfully running the previous step, you should see acquisition dates in the SLC folder and the ".raw" files for each acquisition
Note: For ALOS-1, If there is an acquisition that does not include .raw file, this is most likely due to PRF change between frames and can not be currently handled by ISCE. You have to ignore those.
10- run stackStripmap.py which will generate many config and run files that need to be executed. Here is an example:
stackStripMap.py -s SLC/ -d demLat_S37_S31_Lon_W072_W069.dem.wgs84 -t 250 -b 1000 -a 14 -r 4 -u snaphu
This will produce:
a) baseline folder, which contains baseline information
b) pairs.png which is a baseline-time plot of the network of interferograms
c) configs: which contains the configuration parameter to run different InSAR processing steps
d) run_files: a folder that includes several run and job files that needs to be run in order
11- execute the commands in run files (run_1, run_2, etc) in the run_files folder

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The detailed algorithm for stack processing of TOPS data can be find here:
H. Fattahi, P. Agram, and M. Simons, “A network-based enhanced spectral diversity approach for TOPS time-series analysis,” IEEE Trans. Geosci. Remote Sens., vol. 55, no. 2, pp. 777–786, Feb. 2017. (https://ieeexplore.ieee.org/abstract/document/7637021/)
<<<<<< Sentinel-1 TOPS stack processor >>>>>>
To use the sentinel stack processor, make sure to add the path of your "contrib/stack/topsStack" folder to your $PATH environment varibale.
The scripts provides support for Sentinel-1 TOPS stack processing. Currently supported workflows include a coregistered stack of SLC, interferograms, offsets, and coherence.
stackSentinel.py generates all configuration and run files required to be executed on a stack of Sentinel-1 TOPS data. When stackSentinel.py is executed for a given workflow (-W option) a “configs” and “run_files” folder is generated. No processing is performed at this stage. Within the “run_files” folder different run_#_description files are contained which are to be executed as shell scripts in the run number order. Each of these run scripts call specific configure files contained in the “configs” folder which call ISCE in a modular fashion. The configure and run files will change depending on the selected workflow. To make run_# files executable, change the file permission accordingly (e.g., chmod +x run_1_unpack_slc).
To see workflow examples, type “stackSentinel.py -H”
To get an overview of all the configurable parameters, type “stackSentinel.py -h”
Required parameters of stackSentinel.py include:
-s SLC_DIRNAME A folder with downloaded Sentinel-1 SLC’s.
-o ORBIT_DIRNAME A folder containing the Sentinel-1 orbits.
Missing orbit files will be downloaded automatically
-a AUX_DIRNAME A folder containing the Sentinel-1 Auxiliary files
-d DEM A DEM (Digital Elevation Model) referenced to wgs84
In the following, different workflow examples are provided. Note that stackSentinel.py only generates the run and configure files. To perform the actual processing, the user will need to execute each run file in their numbered order.
In all workflows, coregistration (-C option) can be done using only geometry (set option = geometry) or with geometry plus refined azimuth offsets through NESD (set option = NESD) approach, the latter being the default. For the NESD coregistrstion the user can control the ESD coherence threshold (-e option) and the number of overlap interferograms (-O) to be used in NESD estimation.
------------------------------ Example 1: Coregistered stack of SLC ----------------------------
Generate the run and configure files needed to generate a coregistered stack of SLCs.
In this example, a pre-defined bounding box is specified. Note, if the bounding box is not provided it is set by default to the common SLC area among all SLCs. We recommend that user always set the processing bounding box. Since ESA does not have a fixed frame definition, we suggest to download data for a larger bounding box compared to the actual bounding box used in stackSentinel.py. This way user can ensure to have required data to cover the region of interest. Here is an example command to create configuration files for a stack of SLCs:
stackSentinel.py -s ../SLC/ -d ../DEM/demLat_N18_N20_Lon_W100_W097.dem.wgs84 -a ../../AuxDir/ -o ../../Orbits -b '19 20 -99.5 -98.5' -W slc
by running the command above, the configs and run_files folders are created. User needs to execute each run file in order. The order is specified by the index number of the run file name. For the example above, the run_files folder includes the following files:
- run_1_unpack_slc_topo_master
- run_2_average_baseline
- run_3_extract_burst_overlaps
- run_4_overlap_geo2rdr_resample
- run_5_pairs_misreg
- run_6_timeseries_misreg
- run_7_geo2rdr_resample
- run_8_extract_stack_valid_region
- run_9_merge
- run_10_grid_baseline
The generated run files are self descriptive. Below is a short explanation on what each run_file does:
***run_1_unpack_slc_topo_master:***
Includes commands to unpack Sentinel-1 TOPS SLCs using ISCE readers. For older SLCs which need antenna elevation pattern correction, the file is extracted and written to disk. For newer version of SLCs which don’t need the elevation antenna pattern correction, only a gdal virtual “vrt” file (and isce xml file) is generated. The “.vrt” file points to the Sentinel SLC file and reads them whenever required during the processing. If a user wants to write the “.vrt” SLC file to disk, it can be done easily using gdal_translate (e.g. gdal_translate –of ENVI File.vrt File.slc).
The “run_1_unpack_slc_topo_master” also includes a command that refers to the config file of the stack master, which includes configuration for running topo for the stack master. Note that in the pair-wise processing strategy one should run topo (mapping from range-Doppler to geo coordinate) for all pairs. However, with stackSentinel, topo needs to be run only one time for the master in the stack.
***run_2_average_baseline: ***
Computes average baseline for the stack. These baselines are not used for processing anywhere. They are only an approximation and can be used for plotting purposes. A more precise baseline grid is estimated later in run_10.
***run_3_extract_burst_overlaps: ***
Burst overlaps are extracted for estimating azimuth misregistration using NESD technique. If coregistration method is chosen to be “geometry”, then this run file won’t exist and the overlaps are not extracted.
***run_4_overlap_geo2rdr_resample: ***
Running geo2rdr to estimate geometrical offsets between slave burst overlaps and the stack master burst overlaps. The slave burst overlaps are then resampled to the stack master burst overlaps.
***run_5_pairs_misreg: ***
Using the coregistered stack burst overlaps generated from the previous step, differential overlap interferograms are generated and are used for estimating azimuth misregistration using Enhanced Spectral Diversity (ESD) technique.
***run_6_timeseries_misreg: ***
A time-series of azimuth and range misregistration is estimated with respect to the stack master. The time-series is a least squares esatimation from the pair misregistration from the previous step.
***run_7_geo2rdr_resample: ***
Using orbit and DEM, geometrical offsets among all slave SLCs and the stack master is computed. The goometrical offsets, together with the misregistration time-series (from previous step) are used for precise coregistration of each burst SLC.
***run_8_extract_stack_valid_region: ***
The valid region between burst SLCs at the overlap area of the bursts slightly changes for different acquisitions. Therefore we need to keep track of these overlaps which will be used during merging bursts. Without these knowledge, lines of invalid data may appear in the merged products at the burst overlaps.
***run_9_merge: ***
Merges all bursts for the master and coregistered SLCs. The geometry files are also merged including longitude, latitude, shadow and layer mask, line-of-sight files, etc. .
***run_10_grid_baseline: ***
A coarse grid of baselines between each slave SLC and the stack master is generated. This is not used in any computation.
-------- Example 2: Coregistered stack of SLC with modified parameters -----------
In the following example, the same stack generation is requested but where the threshold of the default coregistration approach (NESD) is relaxed from its default 0.85 value 0.7.
stackSentinel.py -s ../SLC/ -d ../DEM/demLat_N18_N20_Lon_W100_W097.dem.wgs84 -a ../../AuxDir/ -o ../../Orbits -b '19 20 -99.5 -98.5' -W slc -e 0.7
When running all the run files, the final products are located in the merge folder which has subdirectories “geom_master”, “baselines” and “SLC”. The “geom_master” folder contains geometry products such as longitude, latitude, height, local incidence angle, look angle, heading, and shadowing/layover mask files. The “baselines” folder contains sparse grids of the perpendicular baseline for each acquisition, while the “SLC” folder contains the coregistered SLCs
------------------------------ Example 3: Stack of interferograms ------------------------------
Generate the run and configure files needed to generate a stack of interferograms.
In this example, a stack of interferograms is requested for which up to 2 nearest neighbor connections are included.
stackSentinel.py -s ../SLC/ -d ../../MexicoCity/demLat_N18_N20_Lon_W100_W097.dem.wgs84 -b '19 20 -99.5 -98.5' -a ../../AuxDir/ -o ../../Orbits -c 2
In the following example, all possible interferograms are being generated and in which the coregistration approach is set to use geometry and not the default NESD.
stackSentinel.py -s ../SLC/ -d ../../MexicoCity/demLat_N18_N20_Lon_W100_W097.dem.wgs84 -b '19 20 -99.5 -98.5' -a ../../AuxDir/ -o ../../Orbits -C geometry -c all
When executing all the run files, a coregistered stack of slcs are produced, the burst interferograms are generated and then merged. Merged interferograms are multilooked, filtered and unwrapped. Geocoding is not applied. If users need to geocode any product, they can use the geocodeGdal.py script.
-------------------- Example 4: Correlation stack example ----------------------------
Generate the run and configure files needed to generate a stack of coherence.
In this example, a correlation stack is requested considering all possible coherence pairs and where the coregistration approach is done using geometry only.
stackSentinel.py -s ../SLC/ -d ../../MexicoCity/demLat_N18_N20_Lon_W100_W097.dem.wgs84 -b '19 20 -99.5 -98.5' -a ../../AuxDir/ -o ../../Orbits -C geometry -c all -W correlation
This workflow is basically similar to the previous one. The difference is that the interferograms are not unwrapped.
----------------------------------- DEM download example -----------------------------------
Download of DEM (need to use wgs84 version) using the ISCE DEM download script.
dem.py -a stitch -b 18 20 -100 -97 -r -s 1 –c
Updating DEM’s wgs84 xml to include full path to the DEM
fixImageXml.py -f -i demLat_N18_N20_Lon_W100_W097.dem.wgs84

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Users who use the stack processors may refer to the following literatures:
for stripmap stack processor and ionospheric phase estimation:
H. Fattahi, M. Simons, and P. Agram, "InSAR Time-Series Estimation of the Ionospheric Phase Delay: An Extension of the Split Range-Spectrum Technique", IEEE Trans. Geosci. Remote Sens., vol. 55, no. 10, 5984-5996, 2017. (https://ieeexplore.ieee.org/abstract/document/7987747/)
For TOPS stack processing:
H. Fattahi, P. Agram, and M. Simons, “A network-based enhanced spectral diversity approach for TOPS time-series analysis,” IEEE Trans. Geosci. Remote Sens., vol. 55, no. 2, pp. 777–786, Feb. 2017. (https://ieeexplore.ieee.org/abstract/document/7637021/)

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## StripMap stack processor
The detailed algorithms and workflow for stack processing of stripmap SAR data can be found here:
+ Fattahi, H., M. Simons, and P. Agram (2017), InSAR Time-Series Estimation of the Ionospheric Phase Delay: An Extension of the Split Range-Spectrum Technique, IEEE Transactions on Geoscience and Remote Sensing, 55(10), 5984-5996, doi:[10.1109/TGRS.2017.2718566](https://ieeexplore.ieee.org/abstract/document/7987747/).
-----------------------------------
To use the stripmap stack processor, make sure to add the path of your `contrib/stack/stripmapStack` folder to your `$PATH` environment varibale.
Currently supported workflows include a coregistered stack of SLC, interferograms, ionospheric delays.
Here are some notes to get started with processing stacks of stripmap data with ISCE.
#### 1. Create your project folder somewhere
```
mkdir MauleAlosDT111
cd MauleAlosDT111
```
#### 2. Prepare DEM
a) create a folder for DEM;
b) create a DEM using dem.py with SNWE of your study area in integer;
d) Keep only ".dem.wgs84", ".dem.wgs84.vrt" and ".dem.wgs84.xml" and remove unnecessary files;
d) fix the path of the file in the xml file of the DEM by using fixImageXml.py.
```
mkdir DEM; cd DEM
dem.py -a stitch -b -37 -31 -72 -69 -r -s 1 -c
rm demLat*.dem demLat*.dem.xml demLat*.dem.vrt
fixImageXml.py -f -i demLat*.dem.wgs84
cd ..
```
#### 3. Download data
##### 3.1 create a folder to download SAR data (i.e. ALOS-1 data from ASF)
```
mkdir download
cd download
```
##### 3.2 Download the data that that you want to process to the "downlowd" directory
#### 4. Prepare SAR data
Once all data have been downloaded, we need to unzip them and move them to different folders and getting ready for unpacking and then SLC generation. This can be done by running the following command in a directory above "download":
```
prepRawALOS.py -i download/ -o SLC
```
This command generates an empty SLC folder and a run file called: "run_unPackALOS".
You could also run prepRawSensor.py which aims to recognize the sensor data automatically followed by running the sensor specific preparation script. For now we include support for ALOS and CSK raw data, but it is trivial to expand and include other sensors as unpacking routines are already included in the distribution.
```
prepRawSensor.py -i download/ -o SLC
```
#### 5. Execute the commands in "run_unPackALOS" file
If you have a cluster that you can submit jobs, you can submit each line of command to a processor. The commands are independent and can be run in parallel.
After successfully running the previous step, you should see acquisition dates in the SLC folder and the ".raw" files for each acquisition.
Note: For ALOS-1, If there is an acquisition that does not include .raw file, this is most likely due to PRF change between frames and can not be currently handled by ISCE. You have to ignore those.
#### 6. Run "stackStripmap.py"
This will generate many config and run files that need to be executed. Here is an example:
```
stackStripMap.py -s SLC/ -d DEM/demLat*.dem.wgs84 -t 250 -b 1000 -a 14 -r 4 -u snaphu
```
This will produce:
a) baseline folder, which contains baseline information
b) pairs.png which is a baseline-time plot of the network of interferograms
c) configs: which contains the configuration parameter to run different InSAR processing steps
d) run_files: a folder that includes several run and job files that needs to be run in order
#### 7. Execute the commands in run files (run_1*, run_2*, etc) in the "run_files" folder

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The detailed algorithms for stack processing of stripmap data can be found here:
H. Fattahi, M. Simons, and P. Agram, "InSAR Time-Series Estimation of the Ionospheric Phase Delay: An Extension of the Split Range-Spectrum Technique", IEEE Trans. Geosci. Remote Sens., vol. 55, no. 10, 5984-5996, 2017. (https://ieeexplore.ieee.org/abstract/document/7987747/)
-----------------------------------
Notes on stripmap stack processor:
Here are some notes to get started with processing stacks of stripmap data with ISCE.
1- create a folder somewhere for your project
mkdir MauleT111
cd MauleT111
2- create a DEM:
dem.py -a stitch -b -37 -31 -72 -69 -r -s 1 -c
3- Keep only ".dem.wgs84", ".dem.wgs84.vrt" and ".dem.wgs84.xml" and remove unnecessary files
4- fix the path of the file in the xml file of the DEM by using this command:
fixImageXml.py -f -i demLat_S37_S31_Lon_W072_W069.dem.wgs84
5- create a folder to download the ALOS-1 data from ASF:
mkdir download
cd download
6- Download the data that that you want to process to the downlowd directory.
7- once all data have been downloaded, we need to unzip them and move them to different folders and getting ready for unpacking and then SLC generation.
This can be done by running the following command in a directory above "download":
prepRawALOS.py -i download/ -o SLC
This command generates an empty SLC folder and a run file called: "run_unPackALOS".
You could also run prepRawSensor.py which aims to recognize the sensor data automatically followed by running the sensor specific preparation script. For now we include support for ALOS and CSK raw data, but it is trivial to expand and include other sensors as unpacking routines are already included in the distribution.
prepRawSensor.py -i download/ -o SLC
8- execute the commands inside run_unPackALOS file. If you have a cluster that you can submit jobs, you can submit each line of command to a processor. The commands are independent and can be run in parallel.
9- After successfully running the previous step, you should see acquisition dates in the SLC folder and the ".raw" files for each acquisition
Note: For ALOS-1, If there is an acquisition that does not include .raw file, this is most likely due to PRF change between frames and can not be currently handled by ISCE. You have to ignore those.
10- run stackStripmap.py which will generate many config and run files that need to be executed. Here is an example:
stackStripMap.py -s SLC/ -d demLat_S37_S31_Lon_W072_W069.dem.wgs84 -t 250 -b 1000 -a 14 -r 4 -u snaphu
This will produce:
a) baseline folder, which contains baseline information
b) pairs.png which is a baseline-time plot of the network of interferograms
c) configs: which contains the configuration parameter to run different InSAR processing steps
d) run_files: a folder that includes several run and job files that needs to be run in order
11- execute the commands in run files (run_1, run_2, etc) in the run_files folder

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contrib/stack/stripmapStack/stackStripMap.py
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@ -5,7 +5,7 @@
import os, imp, sys, glob
import argparse
import configparser
import datetime
import datetime
import numpy as np
import shelve
import isce
@ -20,7 +20,7 @@ defoMax = '2'
maxNodes = 72
def createParser():
parser = argparse.ArgumentParser( description='Preparing the directory structure and config files for stack processing of Sentinel data')
parser = argparse.ArgumentParser( description='Preparing the directory structure and config files for stack processing of StripMap data')
parser.add_argument('-s', '--slc_directory', dest='slcDir', type=str, required=True,
help='Directory with all stripmap SLCs')
@ -31,7 +31,7 @@ def createParser():
help='Working directory ')
parser.add_argument('-d', '--dem', dest='dem', type=str, required=True,
help='Directory with the DEM (.xml and .vrt files)')
help='DEM file (with .xml and .vrt files)')
parser.add_argument('-m', '--master_date', dest='masterDate', type=str, default=None,
help='Directory with master acquisition')
@ -43,47 +43,54 @@ def createParser():
help='Baseline threshold (max bperp in meters)')
parser.add_argument('-a', '--azimuth_looks', dest='alks', type=str, default='10',
help='Number of looks in azimuth (automaticly computed as AspectR*looks when "S" or "sensor" is defined to give approximately square multi-look pixels)')
help='Number of looks in azimuth (automaticly computed as AspectR*looks when '
'"S" or "sensor" is defined to give approximately square multi-look pixels)')
parser.add_argument('-r', '--range_looks', dest='rlks', type=str, default='10',
help='Number of looks in range')
parser.add_argument('-S', '--sensor', dest='sensor', type=str, required=False,
help='SAR sensor used to define square multi-look pixels')
parser.add_argument('-L', '--low_band_frequency', dest='fL', type=str, default=None,
help='low band frequency')
parser.add_argument('-H', '--high_band_frequency', dest='fH', type=str, default=None,
help='high band frequency')
parser.add_argument('-B', '--subband_bandwidth ', dest='bandWidth', type=str, default=None,
help='sub-band band width')
parser.add_argument('-u', '--unw_method', dest='unwMethod', type=str, default='snaphu'
, help='unwrapping method (icu, snaphu, or snaphu2stage)')
parser.add_argument('-u', '--unw_method', dest='unwMethod', type=str, default='snaphu',
help='unwrapping method (icu, snaphu, or snaphu2stage)')
parser.add_argument('-f','--filter_strength', dest='filtStrength', type=str, default=filtStrength,
help='strength of Goldstein filter applied to the wrapped phase before spatial coherence estimation.'
' Default: {}'.format(filtStrength))
parser.add_argument('--filter_sigma_x', dest='filterSigmaX', type=str, default='100'
, help='filter sigma for gaussian filtering the dispersive and nonDispersive phase')
iono = parser.add_argument_group('Ionosphere', 'Configurationas for ionospheric correction')
iono.add_argument('-L', '--low_band_frequency', dest='fL', type=str, default=None,
help='low band frequency')
iono.add_argument('-H', '--high_band_frequency', dest='fH', type=str, default=None,
help='high band frequency')
iono.add_argument('-B', '--subband_bandwidth ', dest='bandWidth', type=str, default=None,
help='sub-band band width')
parser.add_argument('--filter_sigma_y', dest='filterSigmaY', type=str, default='100.0',
help='sigma of the gaussian filter in Y direction, default=100')
iono.add_argument('--filter_sigma_x', dest='filterSigmaX', type=str, default='100',
help='filter sigma for gaussian filtering the dispersive and nonDispersive phase')
parser.add_argument('--filter_size_x', dest='filterSizeX', type=str, default='800.0',
help='size of the gaussian kernel in X direction, default = 800')
iono.add_argument('--filter_sigma_y', dest='filterSigmaY', type=str, default='100.0',
help='sigma of the gaussian filter in Y direction, default=100')
parser.add_argument('--filter_size_y', dest='filterSizeY', type=str, default='800.0',
help='size of the gaussian kernel in Y direction, default=800')
iono.add_argument('--filter_size_x', dest='filterSizeX', type=str, default='800.0',
help='size of the gaussian kernel in X direction, default = 800')
parser.add_argument('--filter_kernel_rotation', dest='filterKernelRotation', type=str, default='0.0',
help='rotation angle of the filter kernel in degrees (default = 0.0)')
iono.add_argument('--filter_size_y', dest='filterSizeY', type=str, default='800.0',
help='size of the gaussian kernel in Y direction, default=800')
parser.add_argument('-W', '--workflow', dest='workflow', type=str, default='slc'
, help='The InSAR processing workflow : (slc, interferogram, ionosphere)')
iono.add_argument('--filter_kernel_rotation', dest='filterKernelRotation', type=str, default='0.0',
help='rotation angle of the filter kernel in degrees (default = 0.0)')
parser.add_argument('-z', '--zero', dest='zerodop', action='store_true', default=False, help='Use zero doppler geometry for processing - Default : No')
parser.add_argument('--nofocus', dest='nofocus', action='store_true', default=False, help='If input data is already focused to SLCs - Default : do focus')
parser.add_argument('-c', '--text_cmd', dest='text_cmd', type=str, default=''
, help='text command to be added to the beginning of each line of the run files. Example : source ~/.bash_profile;')
parser.add_argument('-useGPU', '--useGPU', dest='useGPU',action='store_true', default=False, help='Allow App to use GPU when available')
parser.add_argument('-W', '--workflow', dest='workflow', type=str, default='slc',
help='The InSAR processing workflow : (slc, interferogram, ionosphere)')
parser.add_argument('-z', '--zero', dest='zerodop', action='store_true', default=False,
help='Use zero doppler geometry for processing - Default : No')
parser.add_argument('--nofocus', dest='nofocus', action='store_true', default=False,
help='If input data is already focused to SLCs - Default : do focus')
parser.add_argument('-c', '--text_cmd', dest='text_cmd', type=str, default='',
help='text command to be added to the beginning of each line of the run files. Example : source ~/.bash_profile;')
parser.add_argument('-useGPU', '--useGPU', dest='useGPU',action='store_true', default=False,
help='Allow App to use GPU when available')
parser.add_argument('--summary', dest='summary', action='store_true', default=False, help='Show summary only')
return parser

63
contrib/stack/stripmapStack/topo.py
View File

@ -1,17 +1,17 @@
#!/usr/bin/env python3
import os
import argparse
import shelve
import datetime
import shutil
import numpy as np
import isce
import isceobj
import numpy as np
import shelve
import os
import datetime
import shutil
from isceobj.Constants import SPEED_OF_LIGHT
from isceobj.Util.Poly2D import Poly2D
from mroipac.looks.Looks import Looks
def createParser():
'''
Command line parser.
@ -48,7 +48,7 @@ class Dummy(object):
def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
from isceobj.Planet.Planet import Planet
from zerodop.GPUtopozero.GPUtopozero import PyTopozero
from isceobj import Constants as CN
@ -84,14 +84,14 @@ def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
omethod = 2 # LEGENDRE INTERPOLATION
else:
omethod = 0 # HERMITE INTERPOLATION
# tracking doppler specifications
if nativedop and (dop is not None):
try:
coeffs = dop._coeffs
except:
coeffs = dop
polyDoppler = Poly2D()
polyDoppler.setWidth(width)
polyDoppler.setLength(length)
@ -109,10 +109,10 @@ def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
polyDoppler.createPoly2D()
# dem
# dem
demImage.setCaster('read','FLOAT')
demImage.createImage()
# slant range file
slantRangeImage = Poly2D()
slantRangeImage.setWidth(width)
@ -130,12 +130,12 @@ def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
dataType = 'DOUBLE'
latImage.initImage(latFilename,accessMode,width,dataType)
latImage.createImage()
# lon file
lonImage = isceobj.createImage()
lonImage.initImage(lonFilename,accessMode,width,dataType)
lonImage.createImage()
# LOS file
losImage = isceobj.createImage()
dataType = 'FLOAT'
@ -144,7 +144,7 @@ def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
losImage.initImage(losFilename,accessMode,width,dataType,bands=bands,scheme=scheme)
losImage.setCaster('write','DOUBLE')
losImage.createImage()
# height file
heightImage = isceobj.createImage()
dataType = 'DOUBLE'
@ -158,7 +158,7 @@ def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
incImage.initImage(incFilename,accessMode,width,dataType,bands=bands,scheme=scheme)
incImage.createImage()
incImagePtr = incImage.getImagePointer()
maskImage = isceobj.createImage()
dataType = 'BYTE'
bands = 1
@ -168,7 +168,7 @@ def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
else:
incImagePtr = 0
maskImagePtr = 0
# initalize planet
elp = Planet(pname='Earth').ellipsoid
@ -214,14 +214,14 @@ def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
topo.set_orbitBasis(1) # Is this ever different?
topo.createOrbit() # Initializes the empty orbit to the right allocated size
count = 0
for sv in info.orbit.stateVectors.list:
td = DTU.seconds_since_midnight(sv.getTime())
pos = sv.getPosition()
vel = sv.getVelocity()
topo.set_orbitVector(count,td,pos[0],pos[1],pos[2],vel[0],vel[1],vel[2])
count += 1
# run topo
topo.runTopo()
@ -244,13 +244,13 @@ def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
# los file
descr = '''Two channel Line-Of-Sight geometry image (all angles in degrees). Represents vector drawn from target to platform.
Channel 1: Incidence angle measured from vertical at target (always +ve).
Channel 1: Incidence angle measured from vertical at target (always +ve).
Channel 2: Azimuth angle measured from North in Anti-clockwise direction.'''
losImage.setImageType('bil')
losImage.addDescription(descr)
losImage.finalizeImage()
losImage.renderHdr()
# dem/ height file
demImage.finalizeImage()
@ -259,7 +259,7 @@ def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
descr = '''Two channel angle file.
Channel 1: Angle between ray to target and the vertical at the sensor
Channel 2: Local incidence angle accounting for DEM slope at target'''
incImage.addDescription(descr)
incImage.finalizeImage()
incImage.renderHdr()
@ -268,7 +268,7 @@ def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
maskImage.addDescription(descr)
maskImage.finalizeImage()
maskImage.renderHdr()
if slantRangeImage:
try:
slantRangeImage.finalizeImage()
@ -276,7 +276,7 @@ def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
pass
def runTopoCPU(info, demImage, dop=None,
def runTopoCPU(info, demImage, dop=None,
nativedop=False, legendre=False):
from zerodop.topozero import createTopozero
from isceobj.Planet.Planet import Planet
@ -298,7 +298,7 @@ def runTopoCPU(info, demImage, dop=None,
topo.numberRangeLooks = info.numberRangeLooks
topo.numberAzimuthLooks = info.numberAzimuthLooks
topo.lookSide = info.lookSide
topo.sensingStart = info.sensingStart + datetime.timedelta(seconds = ((info.numberAzimuthLooks - 1) /2) / info.prf)
topo.sensingStart = info.sensingStart + datetime.timedelta(seconds = ((info.numberAzimuthLooks - 1) /2) / info.prf)
topo.rangeFirstSample = info.rangeFirstSample + ((info.numberRangeLooks - 1)/2) * info.slantRangePixelSpacing
topo.demInterpolationMethod='BIQUINTIC'
@ -331,9 +331,10 @@ def runTopoCPU(info, demImage, dop=None,
topo.topo()
return
def runSimamp(outdir, hname='z.rdr'):
from iscesys.StdOEL.StdOELPy import create_writer
#####Run simamp
stdWriter = create_writer("log","",True,filename='sim.log')
objShade = isceobj.createSimamplitude()
@ -461,9 +462,9 @@ def extractInfo(frame, inps):
info.prf = frame.PRF
info.radarWavelength = frame.radarWavelegth
info.orbit = frame.getOrbit()
info.width = frame.getNumberOfSamples()
info.length = frame.getNumberOfLines()
info.width = frame.getNumberOfSamples()
info.length = frame.getNumberOfLines()
info.sensingStop = frame.getSensingStop()
info.outdir = inps.outdir
@ -472,7 +473,7 @@ def extractInfo(frame, inps):
def main(iargs=None):
inps = cmdLineParse(iargs)
# see if the user compiled isce with GPU enabled
@ -502,11 +503,9 @@ def main(iargs=None):
doppler = db['doppler']
except:
doppler = frame._dopplerVsPixel
db.close()
####Setup dem
demImage = isceobj.createDemImage()
demImage.load(inps.dem + '.xml')
@ -522,7 +521,6 @@ def main(iargs=None):
info.incFilename = os.path.join(info.outdir, 'incLocal.rdr')
info.maskFilename = os.path.join(info.outdir, 'shadowMask.rdr')
runTopo(info,demImage,dop=doppler,nativedop=inps.nativedop, legendre=inps.legendre)
runSimamp(os.path.dirname(info.heightFilename),os.path.basename(info.heightFilename))
@ -540,4 +538,3 @@ if __name__ == '__main__':
Main driver.
'''
main()

129
contrib/stack/topsStack/README.txt → contrib/stack/topsStack/README.md
View File

@ -1,38 +1,80 @@
## Sentinel-1 TOPS stack processor
The detailed algorithm for stack processing of TOPS data can be find here:
H. Fattahi, P. Agram, and M. Simons, “A network-based enhanced spectral diversity approach for TOPS time-series analysis,” IEEE Trans. Geosci. Remote Sens., vol. 55, no. 2, pp. 777–786, Feb. 2017. (https://ieeexplore.ieee.org/abstract/document/7637021/)
+ Fattahi, H., P. Agram, and M. Simons (2016), A Network-Based Enhanced Spectral Diversity Approach for TOPS Time-Series Analysis, IEEE Transactions on Geoscience and Remote Sensing, 55(2), 777-786, doi:[10.1109/TGRS.2016.2614925](https://ieeexplore.ieee.org/abstract/document/7637021).
-----------------------------------
<<<<<< Sentinel-1 TOPS stack processor >>>>>>
To use the sentinel stack processor, make sure to add the path of your "contrib/stack/topsStack" folder to your $PATH environment varibale.
To use the sentinel stack processor, make sure to add the path of your `contrib/stack/topsStack` folder to your `$PATH` environment varibale.
The scripts provides support for Sentinel-1 TOPS stack processing. Currently supported workflows include a coregistered stack of SLC, interferograms, offsets, and coherence.
stackSentinel.py generates all configuration and run files required to be executed on a stack of Sentinel-1 TOPS data. When stackSentinel.py is executed for a given workflow (-W option) a “configs” and “run_files” folder is generated. No processing is performed at this stage. Within the “run_files” folder different run_#_description files are contained which are to be executed as shell scripts in the run number order. Each of these run scripts call specific configure files contained in the “configs” folder which call ISCE in a modular fashion. The configure and run files will change depending on the selected workflow. To make run_# files executable, change the file permission accordingly (e.g., chmod +x run_1_unpack_slc).
`stackSentinel.py` generates all configuration and run files required to be executed on a stack of Sentinel-1 TOPS data. When stackSentinel.py is executed for a given workflow (-W option) a **configs** and **run_files** folder is generated. No processing is performed at this stage. Within the run_files folder different run\_#\_description files are contained which are to be executed as shell scripts in the run number order. Each of these run scripts call specific configure files contained in the “configs” folder which call ISCE in a modular fashion. The configure and run files will change depending on the selected workflow. To make run_# files executable, change the file permission accordingly (e.g., `chmod +x run_1_unpack_slc`).
To see workflow examples, type “stackSentinel.py -H”
To get an overview of all the configurable parameters, type “stackSentinel.py -h”
```bash
stackSentinel.py -H #To see workflow examples,
stackSentinel.py -h #To get an overview of all the configurable parameters
```
Required parameters of stackSentinel.py include:
-s SLC_DIRNAME A folder with downloaded Sentinel-1 SLC’s.
-o ORBIT_DIRNAME A folder containing the Sentinel-1 orbits.
Missing orbit files will be downloaded automatically
-a AUX_DIRNAME A folder containing the Sentinel-1 Auxiliary files
-d DEM A DEM (Digital Elevation Model) referenced to wgs84
```cfg
-s SLC_DIRNAME #A folder with downloaded Sentinel-1 SLC’s.
-o ORBIT_DIRNAME #A folder containing the Sentinel-1 orbits. Missing orbit files will be downloaded automatically
-a AUX_DIRNAME #A folder containing the Sentinel-1 Auxiliary files
-d DEM_FILENAME #A DEM (Digital Elevation Model) referenced to wgs84
```
In the following, different workflow examples are provided. Note that stackSentinel.py only generates the run and configure files. To perform the actual processing, the user will need to execute each run file in their numbered order.
In all workflows, coregistration (-C option) can be done using only geometry (set option = geometry) or with geometry plus refined azimuth offsets through NESD (set option = NESD) approach, the latter being the default. For the NESD coregistrstion the user can control the ESD coherence threshold (-e option) and the number of overlap interferograms (-O) to be used in NESD estimation.
------------------------------ Example 1: Coregistered stack of SLC ----------------------------
Generate the run and configure files needed to generate a coregistered stack of SLCs.
In this example, a pre-defined bounding box is specified. Note, if the bounding box is not provided it is set by default to the common SLC area among all SLCs. We recommend that user always set the processing bounding box. Since ESA does not have a fixed frame definition, we suggest to download data for a larger bounding box compared to the actual bounding box used in stackSentinel.py. This way user can ensure to have required data to cover the region of interest. Here is an example command to create configuration files for a stack of SLCs:
#### AUX_CAL file download ####
The following calibration auxliary (AUX_CAL) file is used for **antenna pattern correction** to compensate the range phase offset of SAFE products with **IPF verison 002.36** (mainly for images acquired before March 2015). If all your SAFE products are from another IPF version, then no AUX files are needed. Check [ESA document](https://earth.esa.int/documents/247904/1653440/Sentinel-1-IPF_EAP_Phase_correction) for details.
Run the command below to download the AUX_CAL file once and store it somewhere (_i.e._ ~/aux/aux_cal) so that you can use it all the time, for `stackSentinel.py -a` or `auxiliary data directory` in `topsApp.py`.
```
wget https://qc.sentinel1.eo.esa.int/product/S1A/AUX_CAL/20140908T000000/S1A_AUX_CAL_V20140908T000000_G20190626T100201.SAFE.TGZ
tar zxvf S1A_AUX_CAL_V20140908T000000_G20190626T100201.SAFE.TGZ
rm S1A_AUX_CAL_V20140908T000000_G20190626T100201.SAFE.TGZ
```
#### 1. Create your project folder somewhere ####
```
mkdir MexicoSenAT72
cd MexicoSenAT72
```
#### 2. Prepare DEM ####
Download of DEM (need to use wgs84 version) using the ISCE DEM download script.
```
mkdir DEM; cd DEM
dem.py -a stitch -b 18 20 -100 -97 -r -s 1 –c
rm demLat*.dem demLat*.dem.xml demLat*.dem.vrt
fixImageXml.py -f -i demLat*.dem.wgs84 #Updating DEM’s wgs84 xml to include full path to the DEM
cd ..
```
#### 3. Download Sentinel-1 data to SLC ####
#### 4.1 Example workflow: Coregistered stack of SLC ####
Generate the run and configure files needed to generate a coregistered stack of SLCs. In this example, a pre-defined bounding box is specified. Note, if the bounding box is not provided it is set by default to the common SLC area among all SLCs. We recommend that user always set the processing bounding box. Since ESA does not have a fixed frame definition, we suggest to download data for a larger bounding box compared to the actual bounding box used in stackSentinel.py. This way user can ensure to have required data to cover the region of interest. Here is an example command to create configuration files for a stack of SLCs:
```
stackSentinel.py -s ../SLC/ -d ../DEM/demLat_N18_N20_Lon_W100_W097.dem.wgs84 -a ../../AuxDir/ -o ../../Orbits -b '19 20 -99.5 -98.5' -W slc
```
by running the command above, the configs and run_files folders are created. User needs to execute each run file in order. The order is specified by the index number of the run file name. For the example above, the run_files folder includes the following files:
- run_1_unpack_slc_topo_master
- run_2_average_baseline
- run_3_extract_burst_overlaps
@ -46,72 +88,83 @@ by running the command above, the configs and run_files folders are created. Use
The generated run files are self descriptive. Below is a short explanation on what each run_file does:
***run_1_unpack_slc_topo_master:***
**run_1_unpack_slc_topo_master:**
Includes commands to unpack Sentinel-1 TOPS SLCs using ISCE readers. For older SLCs which need antenna elevation pattern correction, the file is extracted and written to disk. For newer version of SLCs which don’t need the elevation antenna pattern correction, only a gdal virtual “vrt” file (and isce xml file) is generated. The “.vrt” file points to the Sentinel SLC file and reads them whenever required during the processing. If a user wants to write the “.vrt” SLC file to disk, it can be done easily using gdal_translate (e.g. gdal_translate –of ENVI File.vrt File.slc).
The “run_1_unpack_slc_topo_master” also includes a command that refers to the config file of the stack master, which includes configuration for running topo for the stack master. Note that in the pair-wise processing strategy one should run topo (mapping from range-Doppler to geo coordinate) for all pairs. However, with stackSentinel, topo needs to be run only one time for the master in the stack.
***run_2_average_baseline: ***
**run_2_average_baseline:**
Computes average baseline for the stack. These baselines are not used for processing anywhere. They are only an approximation and can be used for plotting purposes. A more precise baseline grid is estimated later in run_10.
***run_3_extract_burst_overlaps: ***
**run_3_extract_burst_overlaps:**
Burst overlaps are extracted for estimating azimuth misregistration using NESD technique. If coregistration method is chosen to be “geometry”, then this run file won’t exist and the overlaps are not extracted.
***run_4_overlap_geo2rdr_resample: ***
**run_4_overlap_geo2rdr_resample:***
Running geo2rdr to estimate geometrical offsets between slave burst overlaps and the stack master burst overlaps. The slave burst overlaps are then resampled to the stack master burst overlaps.
***run_5_pairs_misreg: ***
**run_5_pairs_misreg:**
Using the coregistered stack burst overlaps generated from the previous step, differential overlap interferograms are generated and are used for estimating azimuth misregistration using Enhanced Spectral Diversity (ESD) technique.
***run_6_timeseries_misreg: ***
**run_6_timeseries_misreg:**
A time-series of azimuth and range misregistration is estimated with respect to the stack master. The time-series is a least squares esatimation from the pair misregistration from the previous step.
***run_7_geo2rdr_resample: ***
**run_7_geo2rdr_resample:**
Using orbit and DEM, geometrical offsets among all slave SLCs and the stack master is computed. The goometrical offsets, together with the misregistration time-series (from previous step) are used for precise coregistration of each burst SLC.
***run_8_extract_stack_valid_region: ***
**run_8_extract_stack_valid_region:**
The valid region between burst SLCs at the overlap area of the bursts slightly changes for different acquisitions. Therefore we need to keep track of these overlaps which will be used during merging bursts. Without these knowledge, lines of invalid data may appear in the merged products at the burst overlaps.
***run_9_merge: ***
**run_9_merge:**
Merges all bursts for the master and coregistered SLCs. The geometry files are also merged including longitude, latitude, shadow and layer mask, line-of-sight files, etc. .
***run_10_grid_baseline: ***
**run_10_grid_baseline:**
A coarse grid of baselines between each slave SLC and the stack master is generated. This is not used in any computation.
#### 4.2 Example workflow: Coregistered stack of SLC with modified parameters ####
-------- Example 2: Coregistered stack of SLC with modified parameters -----------
In the following example, the same stack generation is requested but where the threshold of the default coregistration approach (NESD) is relaxed from its default 0.85 value 0.7.
```
stackSentinel.py -s ../SLC/ -d ../DEM/demLat_N18_N20_Lon_W100_W097.dem.wgs84 -a ../../AuxDir/ -o ../../Orbits -b '19 20 -99.5 -98.5' -W slc -e 0.7
```
When running all the run files, the final products are located in the merge folder which has subdirectories “geom_master”, “baselines” and “SLC”. The “geom_master” folder contains geometry products such as longitude, latitude, height, local incidence angle, look angle, heading, and shadowing/layover mask files. The “baselines” folder contains sparse grids of the perpendicular baseline for each acquisition, while the “SLC” folder contains the coregistered SLCs
When running all the run files, the final products are located in the merge folder which has subdirectories **geom_master**, **baselines** and **SLC**. The **geom_master** folder contains geometry products such as longitude, latitude, height, local incidence angle, look angle, heading, and shadowing/layover mask files. The **baselines** folder contains sparse grids of the perpendicular baseline for each acquisition, while the **SLC** folder contains the coregistered SLCs
#### 4.3 Example workflow: Stack of interferograms ####
------------------------------ Example 3: Stack of interferograms ------------------------------
Generate the run and configure files needed to generate a stack of interferograms.
In this example, a stack of interferograms is requested for which up to 2 nearest neighbor connections are included.
```
stackSentinel.py -s ../SLC/ -d ../../MexicoCity/demLat_N18_N20_Lon_W100_W097.dem.wgs84 -b '19 20 -99.5 -98.5' -a ../../AuxDir/ -o ../../Orbits -c 2
```
In the following example, all possible interferograms are being generated and in which the coregistration approach is set to use geometry and not the default NESD.
```
stackSentinel.py -s ../SLC/ -d ../../MexicoCity/demLat_N18_N20_Lon_W100_W097.dem.wgs84 -b '19 20 -99.5 -98.5' -a ../../AuxDir/ -o ../../Orbits -C geometry -c all
```
When executing all the run files, a coregistered stack of slcs are produced, the burst interferograms are generated and then merged. Merged interferograms are multilooked, filtered and unwrapped. Geocoding is not applied. If users need to geocode any product, they can use the geocodeGdal.py script.
#### 4.4 Example workflow: Stack of correlation ####
-------------------- Example 4: Correlation stack example ----------------------------
Generate the run and configure files needed to generate a stack of coherence.
In this example, a correlation stack is requested considering all possible coherence pairs and where the coregistration approach is done using geometry only.
```
stackSentinel.py -s ../SLC/ -d ../../MexicoCity/demLat_N18_N20_Lon_W100_W097.dem.wgs84 -b '19 20 -99.5 -98.5' -a ../../AuxDir/ -o ../../Orbits -C geometry -c all -W correlation
```
This workflow is basically similar to the previous one. The difference is that the interferograms are not unwrapped.
----------------------------------- DEM download example -----------------------------------
Download of DEM (need to use wgs84 version) using the ISCE DEM download script.
dem.py -a stitch -b 18 20 -100 -97 -r -s 1 –c
Updating DEM’s wgs84 xml to include full path to the DEM
fixImageXml.py -f -i demLat_N18_N20_Lon_W100_W097.dem.wgs84
#### 5. Execute the commands in run files (run_1*, run_2*, etc) in the "run_files" folder ####