Algorithms and utilities for SAR sensors

Related tags

Algorithms sarsen
Overview

WARNING: THIS CODE IS NOT READY FOR USE

Sarsen

Algorithms and utilities for SAR sensors

Objectives

Be faster and simpler than ESA SNAP and cloud native.

  • enable SAR data geocoding
    • fast mode: to terrain-correct images
    • accurate mode: for interferometric processing
  • enable radiometric terrain correction / flattening gamma
  • support cloud-native processing
    • enable parallel processing via xarray and dask
    • enable object storage access via fsspec
  • support Sentinel-1 SLC IW and GRD
  • support any DEM that GDAL / Proj can handle

Non-objectives

  • No attempt is done to support UTC leap seconds. Observations that include a leap second may crash the code or silently return wrong results. Caveat emptor

Usage

>>> import sarsen
Comments
  • sync with latest xarray

    sync with latest xarray

    sarsen is not compatible with the latest (unreleased) xarray. I was able to report and fix a couple of issues upstream (https://github.com/pydata/xarray/issues/6597, https://github.com/pydata/xarray/issues/6600), but we need to apply a few changes in sarsen as well.

    I tested with xarray@main commit c34ef8a60227720724e90aa11a6266c0026a812a, and all tests are successful.

    Let me know if you would like me to add an action that (periodically?) runs the CI using the xarray version under development.

    opened by malmans2 4
  • SARSEN Error

    SARSEN Error

    I have tested on both CentOS 7 and Windows 10. We are unable to use SARSEN because of this error: sarsen gtc S1A_IW_GRDH_1SDV_20210519T094926_20210519T094951_037955_047ACD_7336.zip "IW/VV" GLO20_WGS84_clipped.tif Traceback (most recent call last): File "/opt/anaconda3/envs/SARSEN/bin/sarsen", line 8, in sys.exit(app()) File "/opt/anaconda3/envs/SARSEN/lib/python3.10/site-packages/typer/main.py", line 214, in call return get_command(self)(*args, **kwargs) File "/opt/anaconda3/envs/SARSEN/lib/python3.10/site-packages/click/core.py", line 1128, in call return self.main(*args, **kwargs) File "/opt/anaconda3/envs/SARSEN/lib/python3.10/site-packages/click/core.py", line 1053, in main rv = self.invoke(ctx) File "/opt/anaconda3/envs/SARSEN/lib/python3.10/site-packages/click/core.py", line 1659, in invoke return _process_result(sub_ctx.command.invoke(sub_ctx)) File "/opt/anaconda3/envs/SARSEN/lib/python3.10/site-packages/click/core.py", line 1395, in invoke return ctx.invoke(self.callback, **ctx.params) File "/opt/anaconda3/envs/SARSEN/lib/python3.10/site-packages/click/core.py", line 754, in invoke return __callback(*args, **kwargs) File "/opt/anaconda3/envs/SARSEN/lib/python3.10/site-packages/typer/main.py", line 500, in wrapper return callback(**use_params) # type: ignore File "/opt/anaconda3/envs/SARSEN/lib/python3.10/site-packages/sarsen/main.py", line 17, in gtc apps.backward_geocode_sentinel1( File "/opt/anaconda3/envs/SARSEN/lib/python3.10/site-packages/sarsen/apps.py", line 84, in backward_geocode_sentinel1 measurement_ds = xr.open_dataset( File "/opt/anaconda3/envs/SARSEN/lib/python3.10/site-packages/xarray/backends/api.py", line 495, in open_dataset backend_ds = backend.open_dataset( File "/opt/anaconda3/envs/SARSEN/lib/python3.10/site-packages/xarray_sentinel/xarray_backends.py", line 18, in open_dataset ds = sentinel1.open_sentinel1_dataset( File "/opt/anaconda3/envs/SARSEN/lib/python3.10/site-packages/xarray_sentinel/sentinel1.py", line 715, in open_sentinel1_dataset product_attrs, product_files = esa_safe.parse_manifest_sentinel1(file) File "/opt/anaconda3/envs/SARSEN/lib/python3.10/site-packages/xarray_sentinel/esa_safe.py", line 111, in parse_manifest_sentinel1 manifest = ElementTree.parse(manifest_path) File "/opt/anaconda3/envs/SARSEN/lib/python3.10/xml/etree/ElementTree.py", line 1229, in parse tree.parse(source, parser) File "/opt/anaconda3/envs/SARSEN/lib/python3.10/xml/etree/ElementTree.py", line 580, in parse self._root = parser._parse_whole(source) xml.etree.ElementTree.ParseError: not well-formed (invalid token): line 1, column 2

    opened by S0sh0rt 3
  • Use gamma-0 backscatter convention for RTC output

    Use gamma-0 backscatter convention for RTC output

    Hi all, I was wondering why you chose to stick to the beta-0 backscatter convention instead of gamma-0 for the RTC output? Isn't the entire point of applying David Small's RTC algorithm [1] to get a terrain-flattened, gamma-0 normalized backscatter product? Furthermore, you might have heard of the CEOS CARD4L Normalised Radar Backscatter specification [2], which also endorses the gamma-0 backscatter convention.

    Great software and I'm looking forward to hearing from you!

    [1] https://doi.org/10.1109/tgrs.2011.2120616 [2] https://ceos.org/ard/files/PFS/NRB/v5.5/CARD4L-PFS_NRB_v5.5.pdf

    opened by maawoo 2
  • Radiometric terrain-correction algorithm is slow and memory hungry

    Radiometric terrain-correction algorithm is slow and memory hungry

    At the minimum:

    • [x] review what step can be simplified
    • [x] review in what step we may reduce memory pressure by moving computations to dask out-of-core
    performance 
    opened by alexamici 2
  • Bump mamba-org/provision-with-micromamba from 13 to 14

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    dependencies 
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    v13

    • Fix channels and channel-priority settings
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    dependencies 
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    dependencies 
    opened by dependabot[bot] 0
  • Add option to save the simulated image in SAR and geographic geometries

    Add option to save the simulated image in SAR and geographic geometries

    This is primarily intended as a development tool, as artefacts are more easily detectable on the simulated image, than on the RTC. It may as well allow more precise coregistration between the DEM and the image.

    opened by alexamici 0
  • Make processing dask-friendly to allow larger-than-memory and parallel processing

    Make processing dask-friendly to allow larger-than-memory and parallel processing

    Almost all operations can be chunked via the DEM chunks (not the image ones).

    This includes:

    • [x] allow controlling the DEM chunks
    • [ ] use dask-friendly xarray-sentinel helpers
    • [ ] make internal functions (that need it) dask-friendly via xarray.map_blocks
    • [ ] add borders to the gamma flattening processing to avoid "seeing" the borders of the blocks

    The main source of dask-unfriendliness is the wide spread use of DataArray.interp with the target values on the DEM coordinates. All operations (except the gamma flattening) can be performed on ("x", "y") blocks exactly.

    enhancement 
    opened by alexamici 0
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    dependencies 
    opened by dependabot[bot] 0
  • QST: How to use sarsen functions on other SAR constellations than S1

    QST: How to use sarsen functions on other SAR constellations than S1

    Hello,

    I would like to use sarsen to avoid using ESA SNAP in eoreader, but since this library lacks an API documentation, I have some questions 😄

    I already have my own way to load agnostically these SAR constellations:

    | Constellations | | --- | | COSMO-SkyMed 1st Generation | | COSMO-SkyMed 2nd Generation | | ICEYE | | RADARSAT Constellation Mission | | RADARSAT-2 | | Sentinel-1 | | SAOCOM-1 | | TerraSAR-X, TanDEM-X, PAZ SAR|

    I would like to use your calibration, terrain correction (and so on) methods in order to avoid calling GPT. Is this already possible? (are they fundamentally sensor agnostic ?) Is this a good way to use of your library ? It seems your algorithms are based on xarray-sentinel, but is there a workaround to your functions that would allow anyone to give bands and metadata, allowing the user to easily use your library with other constellations ?

    opened by remi-braun 4
  • DEM format requirements / Reproducible example

    DEM format requirements / Reproducible example

    Hi all, I'm running into issues processing an SLC scene. Both GTC and RTC results are not really usable. I suspect my DEM is the problem and I'm wondering why, because I'm using the same DEM to successfully create an RTC product with SNAP. Here is the gdalinfo output if you're interested.

    Can you provide some guidelines on format requirements for the DEM? Or at least a reproducible example in the notebook section. download_data.sh could, for example, be extended by a section that downloads DEM tiles from an open source (e.g., here) and prepares it with GDAL in the way sarsen expects.

    Cheers, Marco

    opened by maawoo 2
  • Add an option to crop the original product to allow fast testing

    Add an option to crop the original product to allow fast testing

    At the moment GitHub Actions on simple operation fail with a memory error, apparently due to the large size of the input image, even when opened with blocks.

    enhancement 
    opened by alexamici 0
  • Local incidence angle images

    Local incidence angle images

    Hi, I recently discovered sarsen. This is a great tool that fills an important gap in the python geo/rs stack! Thanks!

    I have started to poke around to generate local incidence angle images for GRD products on a target CRS (the one of the DEM), the goal being to have a corrected (geometry and radiometry) SAR image + the local incidence angle (LIA) as a single stack with 3 channels (VV, VH, LIA).

    Actually, I would only need the incidence angle array extracted with xarray-sentinel1 (your other lib) orthorectified on the DEM and then I could use the DEM slope and aspect to compute the local angle.

    Could you give me some guidance in how to generate the incidence angle grid on the DEM geometry? I can't wrap my head around how to use the sarsen API once I get the incidence angles from the gcp subgroup from xarray-sentinel1.

    Thank you.

    opened by inglada 0
Releases(v0.9.3)

  • v0.9.3(Nov 1, 2022)

  • v0.9.2(Jul 27, 2022)

    Algorithms and utilities for Synthetic Aperture Radar (SAR) sensors. Enables cloud-native SAR processing via Xarray and Dask.

    This Open Source project is sponsored by B-Open - https://www.bopen.eu.

    Features and limitations

    Sarsen is a Python library and command line tool with the following functionalities:

    • provides algorithms to terrain-correct satellite SAR data
      • geometric terrain correction (geocoding)
        • fast mode: to terrain-correct images
        • accurate mode: for interferometric processing
      • radiometric terrain correction (gamma flattening)
    • accesses SAR data via xarray-sentinel:
      • supports most Sentinel-1 data products as distributed by ESA:
        • Sentinel-1 Single Look Complex (SLC) SM/IW/EW
        • Sentinel-1 Ground Range Detected (GRD) SM/IW/EW
      • reads uncompressed and compressed SAFE data products on the local computer or on a network via fsspec - depends on rasterio>=1.3
    • accesses DEM data via rioxarray:
      • reads local and remote data in virtually any raster format via rasterio / GDAL
    • supports larger-than-memory and distributed data access and processing via Dask
      • efficient geometric terrain-correction for a full GRD
      • efficient radiometric terrain-correction for a full GRD.

    Overall, the software is in the beta phase and the usual caveats apply.

    Source code(tar.gz)
    Source code(zip)

  • v0.9.0(Apr 26, 2022)

    Algorithms and utilities for Synthetic Aperture Radar (SAR) sensors. Enables cloud-native SAR processing via Xarray and Dask.

    This Open Source project is sponsored by B-Open - https://www.bopen.eu.

    Features and limitations

    Sarsen is a Python library and command line tool with the following functionalities:

    • provides algorithms to terrain-correct satellite SAR data
      • geometric terrain correction (geocoding)
        • fast mode: to terrain-correct images
        • accurate mode: for interferometric processing
      • radiometric terrain correction (gamma flattening)
    • accesses SAR data via xarray-sentinel:
      • supports most Sentinel-1 data products as distributed by ESA:
        • Sentinel-1 Single Look Complex (SLC) SM/IW/EW
        • Sentinel-1 Ground Range Detected (GRD) SM/IW/EW
      • reads uncompressed and compressed SAFE data products on the local computer or on a network via fsspec - depends on rasterio>=1.3a3
    • accesses DEM data via rioxarray:
      • reads local and remote data in virtually any raster format via rasterio / GDAL
    • supports larger-than-memory and distributed data access and processing via Dask

    Overall, the software is in the beta phase and the usual caveats apply.

    Source code(tar.gz)
    Source code(zip)

  • v0.1(Mar 21, 2022)

    Algorithms and utilities for Synthetic Aperture Radar (SAR) sensors. Enables cloud-native SAR processing via Xarray and Dask.

    This Open Source project is sponsored by B-Open - https://www.bopen.eu.

    Overall, the software is in the alpha phase and the usual caveats apply.

    Features and limitations

    Sarsen is a Python library and command line tool with the following functionalities:

    • provides algorithms to terrain-correct satellite SAR data
      • geometric terrain correction (geocoding)
        • fast mode: to terrain-correct images
        • accurate mode: for interferometric processing
      • radiometric terrain correction (gamma flattening)
    • accesses SAR data via xarray-sentinel:
      • supports all Sentinel-1 data products as distributed by ESA:
        • Sentinel-1 Single Look Complex (SLC) SM/IW/EW
        • Sentinel-1 Ground Range Detected (GRD) SM/IW/EW
      • reads uncompressed and compressed SAFE data products on the local computer or on a network via fsspec - depends on rasterio>=1.3a3
    • accesses DEM data via rioxarray:
      • reads local and remote data in virtually any raster format via rasterio / GDAL
    • supports larger-than-memory and distributed data access and processing via dask

    Current limitations and work-in-progress items:

    • little-to-no documentation. See #6.
    • radiometric terrain-correction algorithm slow and memory hungry. See #7.
    Source code(tar.gz)
    Source code(zip)

  • v0.0.13(Mar 9, 2022)

Owner
B-Open
B-Open
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