# temporalCNN

**Repository Path**: menghello/temporalCNN

## Basic Information

- **Project Name**: temporalCNN
- **Description**: Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-03-12
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Temporal Convolutional Neural Network
Training temporal Convolution Neural Netoworks (CNNs) on satelitte image time series.  
This code is supporting by a paper published in Remote Sensing:
```
@article{Pelletier2019Temporal,
  title={Temporal convolutional neural network for the classification of satellite image time series},
  author={Pelletier, Charlotte and Webb, Geoffrey I and Petitjean, Fran{\c{c}}ois},
  journal={Remote Sensing},
  volume={11},
  number={5},
  pages={523},
  year={2019},
  publisher={Multidisciplinary Digital Publishing Institute},
  note={https://www.mdpi.com/2072-4292/11/5/523}
}

```

## Prerequisites
This code relies on Pyhton 3.6 (and should work on Python 2.7) and Keras with Tensorflow backend.


## Examples

### Running the models

- main architecture: `python run_main_archi.py`
- other experiments described in the related paper: 
```
python run_archi.py --sits_path ./ --res_path path/to/results --noarchi 0
```

The architecture (`run_main_archi.py`) will run by training the network on `example/train_dataset.csv` file and by testing it on `example/test_dataset.csv` file.  
Please note that both `train_dataset.csv` and `test_dataset.csv` files are a subsample of the data used in the paper: original data cannot be distributed.

Thoses files have no header, and contain one observation per row having the following format:
`[class,polygonID,date1.NIR,date1.R,date1.G,date2.NIR,...,date149.G]`,
where `class` corresponds to the class label and `polygonID` to a unique polygon identifier for each plot of land.

### Changing network parameters

- Number of channels in the data: `n_channels = 3` (`run_archi.py`, L21).  
It will require to change functions contained in `readingsits.py`.
- Validation rate: `val_rate = 0.05` (`run_archi.py`, L22).
- Network hyperparameters are mainly defined in `architecture_features.py` file.

### Getting predictions for a csv file or a tiff image

```
python write_output.py --model_path path/to/model --test_file path/to/pred.csv --result_file path/to/results/result.csv --proba
```

`test_file` is either a csv file or a tiff image. 
If the `test_file` is a tiff file and `--proba` activated, two tiff images are created: 1) a land cover map, and 2) a tiff image composed of `n_classes` bands that contains the proabbility outputed by the Softmax layer for each class.
The code has been designed to work on small tiff file. Predictions on a big tiff file would require to set up carefully `size_areaX` and `size_areaY` variables (`L86-87` in `write_output.py`).

Please note that the `pred.csv` file should have the same format than `example/train_dataset.csv`, including the `class` field that could be set to `-1`.

### Maps

The produced map for TempCNNs and RFs are available in the `map` folder.


## Contributors
 - [Dr. Charlotte Pelletier](https://sites.google.com/site/charpelletier)
 - [Professor Geoffrey I. Webb](http://i.giwebb.com/)
 - [Dr. Francois Petitjean](http://www.francois-petitjean.com/Research/)