jmichel-otb/s2p

Satellite Stereo Pipeline

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.py. The 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.

Docker image

A precompiled docker image is available and ready to use:

docker pull carlodef/s2p

Usage

Run the python script s2p.py with a json configuration file as unique argument:

python s2p.py test.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 coefficients.

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 json configuration files, relative file path for inputs files and output directory are relative to the json file location, not to the current working directory.

Dependencies

Required dependencies (Ubuntu 16.04):

apt-get install build-essential gdal-bin geographiclib-tools libgeographic-dev libfftw3-dev libgdal-dev libgeotiff-dev libtiff5-dev python python-gdal python-numpy python-pip

and

pip install utm bs4 requests

gdal version must be 2.1.0 or newer.

Installation

git clone https://github.com/MISS3D/s2p.git --recursive
cd s2p
make all

The --recursive option for git clone allows to clone all 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 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 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.

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.