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docker-arcsi

A Docker image packaging Dr Pete Buntings Python Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI) software (https://bitbucket.org/petebunting/arcsi).

This image is based on the official continuumio miniconda python 3.4 release, minimal optimisation and installation of arcsi + dependencies using the conda package manager. Paths and Debian libraries required for proper functioning of ARCSI are updated.

Warning - The resulting image is rather large

Setup

To set up a ARCSI Docker container on your system, first ensure you have Docker installed; follow the instructions at https://docs.docker.com/installation/

To use the image, either pull the latest trusted build from https://registry.hub.docker.com/u/epmorris/docker-arcsi/ by doing this:

docker pull epmorris/docker-arcsi

or build the image yourself like this:

docker build -t epmorris/docker-arcsi https://github.com/edwardpmorris/docker-arcsi

Note: The 'build it yourself' option above will build from the develop branch wheras the trusted builds are against the master branch.

Usage

To run a container and get help on ARCSI commandline options do:

docker run -t epmorris/docker-arcsi arcsi.py -h

To mount a local volume with images, such as freely available USGS Landsat 8 images (available via http://earthexplorer.usgs.gov/), apply radiometric calibration and apply atmospheric correction, for example 'top-of-atmosphere' correction, do:

docker run -i -t \

-v <path_to_local_landsat_folder>:<path_to_local_landsat_folder> \

epmorris/docker-arcsi \

arcsi.py \
-s ls8 \
-f GTiff \
-p RAD TOA \
-i <path_to_local_landsat_folder><landsat_metadata_file>
-o <path_to_local_landsat_folder>

Flag -v tells Docker to mount the specified local volume (in the example this is simply cloned into the container). Replace <path_to_local_landsat_folder> with an absolute path on your filesystem. See Docker user guide, particularily how to add data volumes https://docs.docker.com/userguide/dockervolumes/ . The folder should contain the uncompressed landsat GeoTiff image files and metadata file. At present I did not work out how to include non-local media, such as USBsticks.

Including a command after the container tells Docker to run that command via Bash, here arcsi.py, which requires various options/flags to be defined (see arcsi.py -h). In the example -s defines the sensor, -f the output file format, -p the type of processing, -i the path to a metadata file, -o product output path (in this case the original folder). To try out the command remember to change <landsat_metadata_file> to the relative path of the landsat metadata file (i.e., LC82020352014224LGN00_MTL.txt).

See http://spectraldifferences.wordpress.com/tag/arcsi/ by Dan Clewley and Pete Bunting for a good tutorial on how to use ARCSI via the command line to do atmospheric correction of Landsat images. Support for ARCSI is available via https://bitbucket.org/petebunting/arcsi and rsgislib-support@googlegroups.com. Finally, thanks to the arcsi and rsgislib authors for making their great code publically available.

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