A Generic Bundle Adjustment Methodology for Indirect RPC Model Refinement of Satellite Imagery

Python implementation of A Generic Bundle Adjustment Methodology for Indirect RPC Model Refinement of Satellite Imagery (IPOL, 2021).

Authors: Roger Mari, Carlo de Franchis, Enric Meinhardt-Llopis, Jeremy Anger, Gabriele Facciolo.

Installation

Install the bundle_adjust package:

git clone https://github.com/centreborelli/sat-bundleadjust.git
cd sat-bundleadjust
pip install -e .

Check that the installation was successful by running

pytest tests/test_ba.py

Usage

To run the code:

bundle_adjust config.json

where config.json contains a Python dictionary specifying the paths to the input data and any additional configuration parameters.

Default configuration

To run the default configuration use a config.json as follows:

{
  "geotiff_dir": "your/path/to/the/input/geotiff/images",
  "rpc_dir": "your/path/to/the/input/RPC/models",
  "rpc_src": "txt",
  "output_dir": "your/output/path"
}

where:

geotiff_dir points to the directory containing all the input geotiff image files, with extension .tif
rpc_dir points to the directory containing all the input RPC camera models, in txt files with extension .rpc. The rpcm package is used to represent RPC models, which can be written to txt files using rpcm.RPCModel.write_to_file.
rpc_src is a string that can be either "txt", "json" or "geotiff". If "geotiff" is used, then the input RPC models are directly read from the input geotiff image files.
The output RPC models are written in a folder named rpcs_adj, which is created in the output_dir.

Customized configuration

Check feature_tracks/ft_utils.init_feature_tracks_config for the list of parameters that can be added to config.json to customize the feature tracking stage of the pipeline.

Check ba_pipeline.__init__ for the list of parameters that can be added to config.json to customize the bundle adjustment pipeline.

Test data

Examples:

bundle_adjust tests/config1.json

centreborelli / sat-bundleadjust