Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery

Zhuo Zheng, Yanfei Zhong, Junjue Wang and Ailong Ma

This is an official implementation of FarSeg in our CVPR 2020 paper Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery.

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News

• 2024/03, source code of FarSeg++ is released.
• 2023/10, UV6K dataset is publcily available.
• 2023/07, FarSeg++ is accepted by IEEE TPAMI.

Citation

If you use FarSeg or FarSeg++ in your research, please cite the following paper:

@inproceedings{zheng2020foreground,
  title={Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery},
  author={Zheng, Zhuo and Zhong, Yanfei and Wang, Junjue and Ma, Ailong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={4096--4105},
  year={2020}
}
@article{zheng2023farseg++,
  title={FarSeg++: Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery},
  author={Zheng, Zhuo and Zhong, Yanfei and Wang, Junjue and Ma, Ailong and Zhang, Liangpei},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2023},
  volume={45},
  number={11},
  pages={13715-13729},
  publisher={IEEE}
}

Getting Started

Install SimpleCV

pip install --upgrade git+https://github.com/Z-Zheng/SimpleCV.git

Requirements:

• pytorch >= 1.1.0
• python >=3.6

Prepare iSAID Dataset

ln -s </path/to/iSAID> ./isaid_segm

Evaluate Model

1. download pretrained weight in this link

2. move weight file to log directory

mkdir -vp ./log/isaid_segm/farseg50
mv ./farseg50.pth ./log/isaid_segm/farseg50/model-60000.pth

3. inference on iSAID val

bash ./scripts/eval_farseg50.sh

Train Model

bash ./scripts/train_farseg50.sh