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This repo is used to process sentinel1 data and put COGs in S3

Dockerfile 2.59% Python 32.79% Jupyter Notebook 64.62%
satellite sentinel-1 snap python microwave microwave-remote-sensing

sentinel1-preprocessing's Introduction

Sentinel1-PreProcessing

This repo is used to process sentinel1 data and put COGs in S3

Configurations

  1. Use python 2.7 or 3.3 or 3.4 only

  2. wget https://download.esa.int/step/snap/8.0/installers/esa-snap_sentinel_unix_8_0.sh to download the setup

  3. Then do bash install, Follow the steps

  4. Get Python2.7 bin file, it can be found below.

    • /home/ankush/miniconda/envs/py27/bin/python2.7
    • Then provide python bin path there
  5. Configure python2.7 again

    • cd /home/ankush/snap/bin/
    • Run: bash snappy-conf /home/ankush/miniconda3/envs/py27/bin/python2.7 to register
  6. Fianlly package is installed to path: /home/ankush/.snap/snap-python/snappy

  7. Copy this to our usual site-packages path: cp -r /home/ankush/.snap/snap-python/snappy home/ankush/miniconda3/envs/py27/lib/python2.7/site-packages/

  8. Done, Impory by

    import snappy

Processing Step

  1. Apply orbit file: Updates orbit metadata with a restituted orbit file.
  2. GRD border noise removal: Removes low intensity noise and invalid data on scene edges. (As of January 12, 2018)
  3. Thermal noise removal: Removes additive noise in sub-swaths to help reduce discontinuities between sub-swaths for scenes in multi-swath acquisition modes. (This operation cannot be applied to images produced before July 2015)
  4. Radiometric calibration: Computes backscatter intensity using sensor calibration parameters in the GRD metadata.
  5. Terrain correction (orthorectification): Converts data from ground range geometry, which does not take terrain into account, to σ° using the SRTM 30 meter DEM or the ASTER DEM for high latitudes (greater than 60° or less than -60°).

Resources

  1. Detail Infor about resource, data files: azavea/noaa-flood-mapping#39
  2. Python Snappy Peocessing: https://github.com/wajuqi/Sentinel-1-preprocessing-using-Snappy/blob/master/s1_preprocessing.py
  3. AWS S3 bucket: https://registry.opendata.aws/sentinel-1/
  4. File Name format: https://roda.sentinel-hub.com/sentinel-s1-l1c/GRD/readme.html
  5. Processing using PySAR: johntruckenbrodt/pyroSAR#107
  6. Forum: https://forum.sentinel-hub.com/t/using-aws-s1-grd-data-for-sar-processing/2220/2
  7. Search and Downloading S1 dataser: https://github.com/prodes-amz/aws_imagery_pack
  8. SAFE format specifications:https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/data-formats/safe-specification

Time to Process

  1. Orbit correction - 30.4 s
  2. Thermal Noise Removal - 4min 14s
  3. Calibration - 4min 4s
  4. Multi look - 3min 7s
  5. Terrain Correction - 8min 1s
  6. Ortho rectification - 2min 59s
  7. Speckle Filter - 3min 8s

Total Time - ~26min

sentinel1-preprocessing's People

Contributors

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