S2P - Satellite Stereo Pipeline
This software implements a stereo pipeline which produces elevation models from images taken by high resolution optical satellites such as Pléiades, WorldView, QuickBird, Spot or Ikonos. It generates 3D point clouds and digital surface models from stereo pairs (two images) or tri-stereo sets (three images) in a completely automatic fashion.
S2P was used to win the 2016 IARPA Multi-View Stereo 3D Mapping Challenge.
A wide variety of stereo correlation algorithms are supported, including several flavors of semi-global matching (SGM), TV-L1 optical flow, etc.
The main language is Python, although several operations are handled by binaries written in C.
The pipeline is implemented in the file
s2p module can be used
to produce surface models and 3D point clouds from arbitrarily large regions
of interest or from complete images. If needed, it cuts the region of interest
in several small tiles and process them in parallel.
A precompiled docker image is available and ready to use:
docker pull carlodef/s2p
Run the python script
s2p.py with a json configuration file as unique
python s2p.py testdata/input_pair/config.json
All the parameters of the algorithm, paths to input and output data are stored
in the json file. See the provided
test.json file for an example, and the
comments in the file
s2plib/config.py for some explanations about the roles
of these parameters.
Notice that each input image must be accompanied by an
xml file containing rpc
The processed Region of interest (ROI) is defined by the image coordinates (x,
y) of its top-left corner, and its dimensions (w, h) in pixels. These four
numbers must be given in the
json configuration file, as in the
example file. They are ignored if the parameter
'full_img' is set to
In that case the full image will be processed.
If neither the ROI definition or the
'full_img' flag are present in the
configuration file, then a preview of the reference image must be provided. The
ROI will be selected interactively on that preview. The path of the preview
file must be given by the key
'prv' of the
'images' dictionary (as in
File paths in json configuration files
In the json configuration files, input and output paths are relative to the json file location, not to the current working directory.
Required dependencies (Ubuntu 16.04):
add-apt-repository -y ppa:ubuntugis/ppa # The repository is added so that the version >= 2.1 of gdal is installed (requirement) apt-get update apt-get install build-essential cmake gdal-bin geographiclib-tools libgeographic-dev libfftw3-dev libgdal-dev libgeotiff-dev libtiff5-dev python python-gdal python-numpy python-pip
pip install utm bs4 lxml requests
gdal version must be 2.1.0 or newer.
git clone https://github.com/MISS3D/s2p.git --recursive cd s2p make all
--recursive option for
git clone allows to clone all submodules, such
as the iio library.
--recursive option wasn't used when cloning, the submodules can now be
git submodule update --init
All the python modules are located in the
s2plib folder. Some python
functions of these modules rely on external binaries. Most of these binaries
were written on purpose for the needs of the pipeline, and their source code is
provided here in the
c folder. For the other binaries, the source code is
provided in the
All the sources (ours and 3rdparties) are compiled from the same makefile. Just
make all from the
s2p folder to compile them. This will create a
directory containing all the needed binaries.
You can test if S2P is correctly working using:
If you want to use MicMac for the stereo matching step, you must install it first and create a symlink to the micmac directory (the one containing a 'bin' folder with a bunch of executables in it, among with 'MICMAC' and 'mm3d') in the 'bin' folder:
ln -s PATH_TO_YOUR_MICMAC_DIR bin/micmac
If you use this software please cite the following papers:
An automatic and modular stereo pipeline for pushbroom images, Carlo de Franchis, Enric Meinhardt-Llopis, Julien Michel, Jean-Michel Morel, Gabriele Facciolo. ISPRS Annals 2014.
On Stereo-Rectification of Pushbroom Images, Carlo de Franchis, Enric Meinhardt-Llopis, Julien Michel, Jean-Michel Morel, Gabriele Facciolo. ICIP 2014.
Automatic sensor orientation refinement of Pléiades stereo images, Carlo de Franchis, Enric Meinhardt-Llopis, Julien Michel, Jean-Michel Morel, Gabriele Facciolo. IGARSS 2014.