Detecting Land Cover Changes Between Satellite Image Time Series By Exploiting Self-Supervised Representation Learning Capabilities

This repo hosts the code for the paper title above, accepted for publication at the IGARSS-23, Pasadena, California

Usage

The supervised SITS classification implementation in predetect.py consists of a temporal encoder, a classifier, and post-classification technique for the pre-detection of no-change pixels. The model is trained with labels from one year (2018) and makes predictions on both years. A post-classification technique is used to detect no-change pixels between the predictions. To use predetect.py, you only need to specify the paths to dataset folder for both years:

python predetect.py --dataset_folder1 path_to_2018_dataset --dataset_folder2 path_to_2019_dataset

Once the supervised classification is completed, the best model and the predictions for both years (hard and soft labels) are saved in the output folder; these are input for the contrastive learning module.

To run the contrastive learning train_BYOL.py, specify the paths to the datasets for both years and the folder where the model and predictions are saved. Based on the experiment setup, you can choose to explore other evaluation parameters such as --eval_mode to either freeze or finetune the learned representation in the downstream task, --label_mode to use either the soft pseudo-label, hard pseudo-label or full pixels.

python train_BYOL.py --dataset_folder1 path_to_2018_dataset --dataset_folder2 path_to_2019_dataset --model_dir path_to_saved_model --eval_mode freeze --label_mode softlabel

You can also play around with other model's hyperparameters as specified in the module arguments.

Dataset

A subset of the satellite image time series (SITS) and the annotated label for 2018 and 2019 is available in data/sits. These folders contain the SITS data X, annotated labels y in .npz format and additional data for standardization. The dataset can be read using:

with np.load(PATH_TO_FILE) as f:
    X = f['X']
    y = f['y']

The full dataset can not be distributed here; however, open-source SITS datasets can be found at https://github.com/corentin-dfg/Satellite-Image-Time-Series-Datasets.

Contributors

Adebowale Daniel Adebayo, Charlotte Pelletier, Stefan Lang and Silvia Valero

Citation

If you find this code useful, please cite our work as follows:

@inproceedings{adebayoa23,
  title={Detecting Land Cover Changes Between Satellite Image Time Series By Exploiting Self-Supervised Representation Learning Capabilities},
  author={Adebowale Daniel Adebayo and Charlotte Pelletier and Stefan Lang and Silvia Valero},
  booktitle = {International {Geoscience} and {Remote} {Sensing} {Symposium} ({IGARSS})},
  year={2023}
}

Credits

• The Lightweight Temporal Attention Encoder and the classifier are based on the implementations of Sainte Fare Garnot, Vivien.
• Credits to https://github.com/lucidrains for Bootstrap Your Own Latent (BYOL) in Pytorch.
• And https://github.com/dl4sits for the Temporal Convolutional Neural Network implementation in Pytorch