Awesome

FCD: Fixed-Point GAN for Cloud Detection

PyTorch source code of Nyborg & Assent (2021).

Abstract

The detection of clouds in satellite images is an essential preprocessing task for big data in remote sensing. Convolutional neural networks (CNNs) have greatly advanced the state-of-the-art in the detection of clouds in satellite images, but existing CNN-based methods are costly as they require large amounts of training images with expensive pixel-level cloud labels. To alleviate this cost, we propose Fixed-Point GAN for Cloud Detection (FCD), a weakly-supervised approach. Training with only image-level labels, we learn fixed-point translation between clear and cloudy images, so only clouds are affected during translation. Doing so enables our approach to predict pixel-level cloud labels by translating satellite images to clear ones and setting a threshold to the difference between the two images. Moreover, we propose FCD+, where we exploit the label-noise robustness of CNNs to refine the prediction of FCD, leading to further improvements. We demonstrate the effectiveness of our approach on the Landsat-8 Biome cloud detection dataset, where we obtain performance close to existing fully-supervised methods that train with expensive pixel-level labels. By fine-tuning our FCD+ with just 1% of the available pixel-level labels, we match the performance of fully-supervised methods.

Dependencies

To setup a conda environment named fcd with all dependencies installed, run

conda env create -f environment.yml
conda activate fcd

This will install the following packages:

tqdm
opencv-python
rasterio
tifffile
pillow
matplotlib
pytorch
torchvision
cudatoolkit
tensorboard
albumentations
sklearn
segmentation-models-pytorch

Usage

To download the full Landsat-8 Biome dataset (96 Landsat-8 scenes, about 182 GB when extracted), run

python download_landsat8_biome.py

To prepare 128x128 patches with image-level labels for training, run

python prepare_landsat8_biome.py 

Train FCD

To train Fixed-Point GAN for Cloud Detection (FCD), run

python main.py --mode train --dataset L8Biome --image_size 128 --batch_size 16 --experiment_name FCD

You can monitor the training progress by starting TensorBoard for the runs dir:

tensorboard --logdir=runs

Train FCD+

When FCD is trained, we can generate pixel-level cloud masks for the training dataset by running

python main.py --mode generate_masks --batch_size 64 --experiment_name FCD

This will generate cloud masks for the Landsat-8 scenes in the training dataset, and save them in outputs/FCD/results/tifs. Then, to divide these cloud masks into the corresponding patches for training, we can run

python prepare_landsat8_biome.py --generated_masks outputs/FCD/results/tifs

resulting in a generated_mask.tif in addition to the ground truth mask.tif for every training patch.

Then, to train FCD+ with generated_mask.tif as targets, run

python supervised_main.py --mode train --batch_size 64 --train_mask_file generated_mask.tif \
                          --classifier_head True --experiment_name FCD+

Finally, to fine-tune the resulting model on 1% of actual pixel-wise ground truth, run

python supervised_main.py --mode train --batch_size 64 --keep_ratio 0.01 --lr 1e-5 --freeze_encoder True \
                          --model_weights outputs/FCDPlus/models/best.pt \
                          --experiment_name FCD+1Pct 

Train models compared with in paper

See the bash scripts in the scripts folder for the exact runs done in the paper.

Citation

If you find our work useful for your research, please cite our paper:

@inproceedings{nyborg2021fcd,
  author={Joachim Nyborg and Ira Assent},
  title={Weakly-Supervised Cloud Detection with Fixed-Point GANs}, 
  booktitle={2021 {IEEE} International Conference on Big Data (Big Data)},
  pages={4191--4198},
  publisher={{IEEE}},
  year={2021},
  doi={10.1109/BigData52589.2021.9671405}
}

Acknowledgements

This repository is based on mahfuzmohammad/Fixed-Point-GAN and yunjey/stargan.