S2P - Satellite Stereo Pipeline

S2P is a Python library and command line tool that 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 Python package s2p. It 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.

Its main source code repository is https://github.com/miss3d/s2p.

Installation and dependencies

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-numpy python-pip

gdal version must be 2.1.0 or newer.

For users: install s2p from PyPI

pip install s2p

For developers: install s2p from sources

git clone https://github.com/MISS3D/s2p.git --recursive
cd s2p
pip install -e .

The --recursive option for git clone allows to clone all git submodules, such as the iio library.

If the --recursive option wasn't used when cloning, the submodules can now be retrieved with

git submodule update --init

All s2p python submodules are located in the s2p package. 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 3rdparty folder.

All the sources (ours and 3rdparties) are compiled from the same makefile. Just run make all from the s2p folder to compile them. This will create a bin directory containing all the needed binaries. This makefile is used when running pip install .

You can test if S2P is correctly working using:

make test

Docker image

A precompiled docker image is available and ready to use:

docker pull cmla/s2p

Usage

s2p is a Python library that can be imported into other applications. It also comes with a Command Line Interface (CLI).

From the command line

The s2p CLI has an extensive help that can be printed with the -h and --help switches.

$ s2p -h
usage: s2p.py [-h] config.json

S2P: Satellite Stereo Pipeline

positional arguments:
  config.json           path to a json file containing the paths to input and
                        output files and the algorithm parameters

optional arguments:
  -h, --help            show this help message and exit

To run the whole pipeline, call s2p with a json configuration file as unique argument:

s2p tests/data/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 s2p/config.py for some explanations about the roles of these parameters.

Notice that each input image must have RPC coefficients, either in its GeoTIFF tags or in a companion .xml or .txt file.

ROI definition

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 test.json example file. They are ignored if the parameter 'full_img' is set to true. 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'[0] dictionary (as in the example).

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.

MicMac (optional)

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

References

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.