Awesome

Self-supervised Vision Transformers for Land-cover Segmentation and Classification

This project aims to provide land-cover classification and segmentation of Satellite images using Deep Learning, by introducing a self-supervised pre-trained Swin Transformer. The training is done in two stages; first, self-supervised training of one unique backbone is performed on a large unlabeled dataset, second, supervised fine-tuning of this backbone is performed on a small, labeled dataset, for two separate downstream tasks.

In this repository, we make the code built for this project (implemented in PyTorch, version 1.11) and pretrained backbones for Sentinel-1 and Sentinel-2 data available to the community. The pretrained backbones are task agnostic and therefore can be readily deployed and fine-tuned to a range of downstream tasks utilizing Sentinel-1 or Sentinel-2 data, or a fusion of both.

Qualitative comparison of ours segmentation results with different methods (ours: SwinUNet SSL-ft)

Self-supervised Training

ResNet50

The script train_resnet_backbone.py trains a model with two ResNet50 backbones for Sentinel-1/2 inputs. The the path to the SEN12MS dataset can be supplied with the --train_dir flag.

Swin-t

The Swin transformer backbones are trained with the train_d_swin_backbone.py script. The model code is adapted from here. Changes to the default parameters, e.g. path to the training data, can be made by adjusting the values in the config file at configs/backbone_config.json.

Downstream Tasks

Fine-tuning and linear evaluation on the classification tasks is done with train_evaluation.py. The downstream_eval_scripts/ directory contains small bash scripts to evaluate the different settings covered in the paper.

You can use the pre-trained weights (downloaded from the Pretrained models) and then run for example:

# Single-label Classification
python train_evaluation.py --model=DoubleSwinTransformerDownstream --image_px_size=224 --batch_size=8 --checkpoint=/path/to/checkpoint --target dfc_label
# Multi-label Classification
python train_evaluation.py --model=DoubleSwinTransformerDownstream --image_px_size=224 --batch_size=8 --checkpoint=/path/to/checkpoint --target dfc_multilabel_one_hot
# Segmentation
python train_evaluation.py --model=DoubleSwinTransformerSegmentation --image_px_size=224 --batch_size=8 --checkpoint=/path/to/checkpoint --target dfc

Data

The datasets used in this work are publicly available:

SEN12MS (self-supervised pre-training)
DFC2020 (downstream land-cover classification/segmentation)

Pretrained models

We provide pretrained ResNet50 and Swin-t transformer backbones trained with Sentinel-1/2 pairs from SEN12MS (self-supervised):

These backbones can be readily fine-tuned to a range of downstream tasks that utilize Sentinel-1 data, Sentinel-2 data or the fusion of both. We provide example Jupyter Notebooks for utilizing these pretrained backbones:

demo/demo_resnet_backbone.ipynb, for classification using a self-supervised pre-trained ResNet backbone, utilizing Sentinel-2 multi-band imaging data, only;
demo/demo_swin_backbone.ipynb, for classification using a self-supervised pre-trained SwinTransformer backbone, utilizing a fusion of Sentinel-1 and Sentinel-2 data.

Acknowledging this work

If you would like to cite our work, please use the following reference:

Scheibenreif, Hanna, Mommert, Borth, Self-supervised Vision Transformers for Land-cover Segmentation and Classification, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 1422-1431

and/or

Scheibenreif, Mommert, Borth, Contrastive Self-Supervised Data Fusion for Satellite Imagery, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume V-3-2022

Code

This repository incorporates code from the following sources: