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.
News
- 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