Package: gms-preprocessing
The goal of the gms_preprocessing Python library is to provide a fully automatic
pre-precessing pipeline for spatial and spectral fusion (i.e., homogenization)
of multispectral satellite image data. Currently it offers compatibility to
Landsat-5, Landsat-7, Landsat-8, Sentinel-2A and Sentinel-2B.Free software: GNU General Public License v3 or later (GPLv3+) (license details)Documentation:https://geomultisens.gitext-pages.gfz-potsdam.de/gms_preprocessing/doc/Code history: Release notes for the current and earlier versions of gms_preprocessing can be foundhere.OS compatibility: LinuxStatusSee also the latestcoveragereport and thenosetestsHTML report.FeaturesLevel-1 processing:data import and  metadata homogenization (compatibility: Landsat-5/7/8, Sentinel-2A/2B)equalization of acquisition- and illumination geometryatmospheric correction (usingSICOR)correction of geometric errors (usingAROSICS)Level-2 processing:spatial homogenizationspectral homogenization (usingSpecHomo)estimation of accuracy layers=> application oriented analysis datasetGetting startedUsage via WebAppThe recommended way to use gms_preprocessing is to setup the WebApp (see thegms-visrepository) providing a UI for GeoMultiSens. Using this UI, existing
satellite data can be explored, filtered and selected for processing. New data
homogenization jobs (using gms_preprocessing) can be defined and started. All
configuration parameters of gms_preprocessing are accessible in the UI.Usage via console interfaceHomogenization jobs can also be created and started using the command line
interface. Documentation can be foundhere.Here is a small example:# start the job with the ID 123456 and override default configuration with the given one.>>>run_gms.pyjobid123456--json_config/path/to/my/config.jsonThere is a default configuration file, calledoptions_default.json.
This file contains the documentation for all the available configuration
parameters.Usage via Python APIThere is also a Python API that allows to setup and start homogenization jobs
by a Python function call.This is an example:fromgms_preprocessingimportProcessControllerconfiguration=dict(db_host='localhost',CPUs=20)PC=ProcessController(job_ID=123456,**configuration)PC.run_all_processors()Possible configuration arguments can be foundhere.History / ChangelogYou can find the protocol of recent changes in the gms_preprocessing packagehere.Licensegms_preprocessing - Spatial and spectral homogenization of satellite remote sensing data.Copyright 2020 Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, GermanyThis program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version. This program is distributed in the hope
that it will be useful, but WITHOUT ANY WARRANTY; without even the implied
warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details. You should have received a copy
of the GNU General Public License along with this program.
If not, see <http://www.gnu.org/licenses/>.ContactDaniel SchefflereMail:daniel.scheffler@gfz-potsdam.deHelmholtz Centre Potsdam GFZ German Research Centre for GeoscienesSection 1.4 Remote SensingTelegrafenberg14473 PotsdamGermanyCreditsThe development of the gms_preprocessing package was funded by the German Federal Ministry of Education and Research
(BMBF, project grant code: 01 IS 14 010 A-C).The package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.Landsat-5/7/8 satellite data and SRTM/ASTER digital elevation models have been provided by the US Geological
Survey. Sentinel-2 data have been provided by ESA.