Commit 8a0f57de authored by emanueledalsasso's avatar emanueledalsasso
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Uploaded reference and colab

parent dfae03b6
......@@ -5,16 +5,19 @@ _Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) ima
![summary_SAR-CNN](./img/proposedCNN.png)
## Resources
## Resources
- [Paper (ArXiv)](https://arxiv.org/abs/2006.15559)
The material is made available under the **GNU General Public License v3.0**: Copyright 2020, Emanuele Dalsasso, Loïc Denis, Florence Tupin, of LTCI research lab - Télécom ParisTech, an Institut Mines Télécom school.
All rights reserved.
- [Remot Sensing publication](https://www.mdpi.com/2072-4292/12/16/2636)
To cite the article:
```
Dalsasso, E.; Yang, X.; Denis, L.; Tupin, F.; Yang, W.
SAR Image Despeckling by Deep Neural Networks: from a Pre-Trained Model to an End-to-End Training Strategy.
Remote Sens. 2020, 12, 2636. https://doi.org/10.3390/rs12162636
```
@article{dalsasso2020sar,
title={SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy},
author={Emanuele Dalsasso and Xiangli Yang and Loïc Denis and Florence Tupin and Wen Yang},
journal={arXiv preprint arXiv:2006.15559},
year={2020}
}
## Licence
The material is made available under the **GNU General Public License v3.0**: Copyright 2020, Emanuele Dalsasso, Loïc Denis, Florence Tupin, of LTCI research lab - Télécom ParisTech, an Institut Mines Télécom school.
All rights reserved.
%% Cell type:markdown id: tags:
<a href="https://colab.research.google.com/github/emanueledalsasso/SAR-CNN/blob/master/SAR_CNN_test.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
**[Download the notebook](https://gitlab.telecom-paris.fr/ring/SAR-CNN/-/raw/master/SAR_CNN_test.ipynb?inline=false) and then import it under Google Colab**
<a href="https://colab.research.google.com/" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
%% Cell type:markdown id: tags:
# SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy
## Emanuele Dalsasso, Xiangli Yang, Loïc Denis, Florence Tupin, Wen Yang
The code is made available under the **GNU General Public License v3.0**: Copyright 2020, Emanuele Dalsasso, Loïc Denis, Florence Tupin, of LTCI research lab - Télécom ParisTech, an Institut Mines Télécom school.
All rights reserved.
Please note that the training set is only composed of **Sentinel-1** SAR images, thus this testing code is specific to this data.
%% Cell type:markdown id: tags:
## Resources
- [Paper (ArXiv)](https://arxiv.org/abs/2006.15559)
- [Remot Sensing publication](https://www.mdpi.com/2072-4292/12/16/2636)
To cite the article:
@article{dalsasso2020sar,
title={SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy},
author={Emanuele Dalsasso and Xiangli Yang and Loïc Denis and Florence Tupin and Wen Yang},
journal={arXiv preprint arXiv:2006.15559},
year={2020}
}
```
Dalsasso, E.; Yang, X.; Denis, L.; Tupin, F.; Yang, W.
SAR Image Despeckling by Deep Neural Networks: from a Pre-Trained Model to an End-to-End Training Strategy.
Remote Sens. 2020, 12, 2636. https://doi.org/10.3390/rs12162636
```
%% Cell type:markdown id: tags:
## 0. Enable GPU and save copy on Drive to enable editing
Runtime -> Change runtime type -> Hardware accelerator: GPU
File -> Save a copy in Drive
%% Cell type:markdown id: tags:
## 1. Download network weights and code
%% Cell type:code id: tags:
```
from google_drive_downloader import GoogleDriveDownloader as gdd
gdd.download_file_from_google_drive(file_id='1CgoG3f02uFzpA5PGcwKek9bitp_5T64q',
dest_path='./SAR-CNN-test.zip',
unzip=True)
```
%% Cell type:markdown id: tags:
## 2. Install compatible version of tensorflow
%% Cell type:code id: tags:
```
!pip install tensorflow-gpu==1.13.1
```
%% Cell type:markdown id: tags:
## A. Test on synthetic data
%% Cell type:code id: tags:
```
!python /content/SAR-CNN-test/main.py --test_dir=/content/test_synthetic
```
%% Cell type:markdown id: tags:
## B. Test on real data
Two real Sentinel-1 images can be found in the folder _/content/SAR-CNN-test/test_data/real_
To test on custom data, upload your single channel Sentinel-1 images in a numpy array with shape [ydim, xdim].
Results are stored in _/content/test_
At each time a test is run, clean the _/content/test_ directory otherwise the results will be overwritten.
%% Cell type:code id: tags:
```
!python /content/SAR-CNN-test/main.py --real_sar=1 --test_dir=/content/test_real
```
......
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