Phi-Net is a novel deep learning methodology for the joint estimation of the Interferometric Synthetic Aperture Radar (InSAR) phase and coherence.
Phi-Net is trained using synthetic data obtained by an innovative strategy based on the theoretical modeling of the physics behind the SAR acquisition principle. This strategy allows the network to generalize the estimation problem with respect to:
- different noise levels,
- the nature of the imaged target on the ground,
- the interferometric acquisition geometry.
The code has been developed at the Microwaves and Radar Institute of the German Aerospace Center (DLR). Münchener Str. 20, 82234 Weßling.
The present code is implemented on the basis of the paper:
- F. Sica, G. Gobbi, P. Rizzoli and L. Bruzzone, Φ-Net: Deep Residual Learning for InSAR Parameters Estimation, in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.3020427.
If you use this code in your own research, please cite our paper describing it.
BibTex @ARTICLE{Sica2020,
author={F. {Sica} and G. {Gobbi} and P. {Rizzoli} and L. {Bruzzone}},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={ϕ-Net: Deep Residual Learning for InSAR Parameters Estimation},
year={2020},
volume={},
number={},
pages={1-25},
doi={10.1109/TGRS.2020.3020427}}
- Giorgia Gobbi
- Francescopaolo Sica
- phinet/data: input files master.npy and slave.npy SAR Single Look Complex.
- phinet/trained_model: Phi-Net trained model phi_net_model.hdf5.
- phinet/result: output files estimated_phase.npy and estimated_coherence.npy.
Create environment from YAML:
conda env create --name phinet_env --file requirements.yml
Activate environment:
source activate phinet_env
Launch test:
python test_demo.py
Visualize results obtained from the test:
python visualize_results.py
The requirements.yml file lists all packages necessary to run the
test_demo.py file in the phinet folder.
This package is developed under Python 3.7 with tensorflow 1.13.1.
The authors would like to acknowledge Philipp Posovszky and Andrea Pulella for their support in the set up of this repository.
Copyright © 2020 Deutsches Zentrum für Luft und Raumfahrt e.V.
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