REM SatEx Plugin

This is a QGIS plugin which streamlines a pixel-based classifiaction of Landsat imagery using the OrfeoToolbox (OTB). Only required inputs are:

a directory containing the band raster files of one or more Landsat satellite image,
a region of interest as a polygon in a shapefile, and
a vector containing a polygon feature set with training/testing information

Optionally a trained SVM model for the libsvm implementation of OTB, as also created by the plugin can be provided instead of (3).

The plugin requires QGIS (>=2.8) and an installation of the Orfeo Toolbox (OTB)

The plugin requires an installation of the Orfeo Toolbox (OTB; details: www.orfeo-toolbox.org). On Windows you can install it via OSGeo4W on Linux you can install it from packages provided by your distribution or build it from the source packages available from its git repository.

Note: Some Linux distributions split OTB in different packages, in order for this plugin to work make sure the python wrappers are installed alongside with the otb library. You can check if OTB and the wrappers are installed from within qgis by opening the Python Console and typing (without the >):

import otbApplication
otbApplication.Registry.GetAvailableApplications()

This should return you a list of otb functions if it's working.

In order to build the documentation the python library sphinx is required.

Purpose of the Plugin

Plugin that provides two algorithms for the processing of one or multiple Landsat scenes within a region of interest towards a Landuse/Landcoverage classification streamlining all required processing steps to perform a libsvm/orfeo toolbox (OTB) pixel based classification.

Structure of the Plugin

The Plugin is structured in two modules:

Preprocessing, and
Classification

In the "Preprocessing" algorithm Landsat scenes located in a directory as , e.g., the directory created by extracting from the downloaded zip archive of a Landsat 8 scene as can be found on EarthExplorer http://earthexplorer.usgs.gov/ is

cropped to a region of interest provided as , e.g., a polygon feature in a vector file and then
the separate spectral Bands are stacked and
a virtual raster tile is created out of these, i.e., in case the region of interest stretches over more than one Landsat scene. If present, the panchromatic band 8 (Landsat 7 and 8) is excluded from the layers. The "Classification" algorithm is performing a classification of a raster file as, e.g., resulting from the "Preprocessing" algorithm and either by using a provided trained Support Vector Model (SVM) from OTB or training and testing a SVM on the fly using a provided ground truth testing/training data set. In the case a on the fly training/testing is performed the provided ground truth data is randomly split in a testing (~20%) and a training part (~80%), the latter is then used in the libsvm implementation of OTB to create a SVM. This SVM (or the external SVM) are then used to classify the image. The resulting raster file with class labels is then tested with the testing dataset (all features of the provided vector layer in case an external SVM model was provided) and a confusion matrix is produced. Finally the resulting raster file is sieved (i.e., regions consisting of view pixels are merged to the surrounding).

You can find further informations on the usage of the plugin in the enclosed plugin documentation by pressing the help button in one of the dialogs or opening ($HOME/.qgis2/python/plugins/SatEx/help/build/html/index.html).

Testing data

In the test/test_data you will find a directory 'satellite_scenes' containing a subset of 2 satellite scences, an example region of interest roi.shp and an example training/testing vector 'train.shp'. These can be used for testing the funtionality of the plugin.

Contact

Michael Haas - mhaas(at)gfz-potsdam.de Massimiliano Pittore - pittore(at)gfz-potsdam.de GFZ German Research Centre for Geosciences - Centre for Early Warning Systems

License

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
Neither the name of REM_satex_plugin nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

GFZ-Centre-for-Early-Warning/REM_satex_plugin