Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/janne-alatalo/sar-change-detection

Code for the paper "Improved Difference Images for Change Detection Classifiers in SAR Imagery Using Deep Learning".
https://github.com/janne-alatalo/sar-change-detection

Last synced: about 2 months ago
JSON representation

Code for the paper "Improved Difference Images for Change Detection Classifiers in SAR Imagery Using Deep Learning".

Awesome Lists containing this project

README 

        
# Improved Difference Images for Change Detection Classifiers in SAR Imagery Using Deep Learning



This repository includes code for the paper **Improved Difference Images for Change Detection Classifiers in SAR Imagery Using Deep Learning**. Published in IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 61, 2023, [https://doi.org/10.1109/TGRS.2023.3324994](https://doi.org/10.1109/TGRS.2023.3324994) (open-access, also available in the `paper` folder in this repository).



## Download the dataset



The dataset is downloadable from here [https://doi.org/10.23729/7b22c271-5e25-40fe-aa6a-f5d0330b1872](https://doi.org/10.23729/7b22c271-5e25-40fe-aa6a-f5d0330b1872).



## Install dependencies



```

pip install -r requirements.txt

```



## Preprocess the dataset



The dataset must be preprocessed with `preprocess_tfrecords.py` script before

training the model. Change `/path/to` in the filepaths to the actual location

where you want to store the files.



```

python preprocess_tfrecords.py --dataset_stats /path/to/stats.json --output_dir /path/to/processed/train/ input_file.tfrecord.GZIP

```



You might want to use [GNU parallel](https://www.gnu.org/software/parallel/) or similar to parallellize the processing e.g.



```

# assuming that train_files.txt includes filepaths to all files you want to preprocess

parallel --halt now,fail=1 -j 45 -I{} python preprocess_tfrecords.py --dataset_stats /path/to/stats.json --output_dir /path/to/processed/train/ {} :::: train_files.txt

```



Do same for the validation data.



```

# assuming that val_files.txt includes filepaths to all files you want to preprocess

parallel --halt now,fail=1 -j 45 -I{} python preprocess_tfrecords.py --dataset_stats /path/to/stats.json --output_dir /path/to/processed/val/ {} :::: val_files.txt

```



## Train the neural network



```

python main.py \

        --train_data "/path/to/processed/train/records/*.tfrecord.GZIP" \

        --val_data "/path/to/processed/val/records/*.tfrecord.GZIP" \

        --no_checkpoints \

        --epochs 50

```



The training script is logging the training metrics to `./logs` directory. You can see the logs with tensorboard.



```

tensorboard --logdir logs/ --samples_per_plugin "images=50"

```



Epoch loss plot with the default parameters (blue = train, red = validation):



![Epoch loss plot](./figs/epoch-loss.png)



## Computing the classifier accuracies



### Use the trained model checkpoint to add predictions to the dataset



```

python generate_change_dataset_with_predictions.py \

        --model_checkpoint "logs/CHECK_THE_CORRECT_DIR/checkpoints/final" \

        --output_dir "/path/to/simulated-change-with-prediction/" \

        "/path/to/val/records/*.tfrecord.GZIP"

```



### Compute the results



For threshold classifier:



```

python threshold_classifier.py  --save_filename minus2_5dB-shift.png --simulated_change_shift -2.5 "/path/to/simulated-change-with-prediction/*.tfrecord.GZIP"

```



And for SVC classifier:



```

python svm_classifier.py "/path/to/simulated-change-with-prediction/*.tfrecord.GZIP"

```



## Simulated change



This repository includes the `generate_change_dataset.py` script that was used

to generate the simulated change dataset for the experiments. However, the

script is too tightly coupled with the database for it to be executable

anywhere. The script requires access to the PostgreSQL database that stores the

SAR image rasters, and the database is too large to be shareable. However, you

can request the dataset that was used to run the experiments. The dataset

includes the simulated changes for the validation samples.



## Ablation Study



An ablation study was conducted for the different features to see what features

are important for the model. The experiment can be repeated using the

`--ablation_study` argument for the `main.py` script (e.g. `python main.py

--ablation_study snow_depth` trains the neural network without the snow depth

feature). The resulting validation loss plot from the different runs is

interesting, but did not fit to the paper, therefore it is presented here. The

loss clearly shows that the 'Orbit Direction' feature is the most important

one, and without it the validation loss is clearly higher than when the other

features are removed. 'Temperature', 'Precipitation', and 'Incidence Angle' are

also important features. Removing the 'Snow Depth' and 'Satellite Id' features

did not have much effect to the resulting validation loss as removing the other

features.



![Epoch loss plot](./figs/ablation-study.png)



## Citation



```

@ARTICLE{10286479,

  author={Alatalo, Janne and Sipola, Tuomo and Rantonen, Mika},

  journal={IEEE Transactions on Geoscience and Remote Sensing}, 

  title={Improved Difference Images for Change Detection Classifiers in SAR Imagery Using Deep Learning}, 

  year={2023},

  volume={61},

  number={},

  pages={1-14},

  doi={10.1109/TGRS.2023.3324994}}

```



## Acknowledgements



This research was conducted in Jamk University of Applied Sciences and was funded

in part by the Regional Council of Central Finland/Council of Tampere Region and European Regional Development Fund as part of the project [*Data for Utilisation -- Leveraging digitalisation through modern artificial intelligence solutions and cybersecurity*](https://www.jamk.fi/en/research-and-development/rdi-projects/data-for-utilisation-leveraging-digitalisation-through-modern-artificial-intelligence-solutions-and) (grant number A76982),

in part by the REACT-EU Instrument as part of the European Union’s response to the COVID-19 pandemic as part of the project [*coADDVA - ADDing VAlue by Computing in Manufacturing*](https://www.jamk.fi/en/research-and-development/rdi-projects/coaddva-adding-value-by-computing-in-manufacturing) (grant number A77973),

and in part by the European Union and the Regional Council of Central Finland with the Just Transition Fund as part of the project [*Finnish Future Farm*](https://www.jamk.fi/en/research-and-development/rdi-projects/finnish-future-farm) (grant number J10075).