2025/2/11: LOGCAN++ has been accepted by TGRS2025!2025/1/24: SCSM has been accepted by ISPRS2025!2024/10/11: SSA-Seg has been accepted by NeurIPS2024! It is an effective and powerful classifier for semantic segmentation. We recommend interested researchers to optimize it for semantic segmentation in remote sensing, which is a promising direction.2024/06/24: LOGCAN++ has been submitted to Arxiv, which is an extension of our previous conference paper LOGCAN. The official implementation of LOGCAN++ is available!
rssegmentation is an open-source semantic segmentation toolbox, which is dedicated to reproducing and developing advanced methods for semantic segmentation of remote sensing images.
- Supported Methods
- Supported Datasets
- Supported Tools
- Training
- Testing
- Params and FLOPs counting
- Class activation maps (Updated soon)
conda create -n rsseg python=3.9
conda activate rsseg
conda install pytorch==2.0.0 torchvision==0.15.0 torchaudio==2.0.0 pytorch-cuda=11.7 -c pytorch -c nvidia
pip install -r requirements.txtPrepare the following folders to organize this repo:
rssegmentation
├── rssegmentation (code)
├── work_dirs (save the model weights and training logs)
├── data
│ ├── LoveDA
│ │ ├── Train
│ │ │ ├── Urban
│ │ │ │ ├── images_png (original images)
│ │ │ │ ├── masks_png (original labels)
│ │ │ ├── Rural
│ │ │ │ ├── images_png (original images)
│ │ │ │ ├── masks_png (original labels)
│ │ ├── Val (the same with Train)
│ │ ├── Test
│ ├── vaihingen
│ │ ├── ISPRS_semantic_labeling_Vaihingen
│ │ │ ├── top (original images)
│ │ ├── ISPRS_semantic_labeling_Vaihingen_ground_truth_COMPLETE (original labels)
│ │ ├── ISPRS_semantic_labeling_Vaihingen_ground_truth_eroded_COMPLETE (original noBoundary lables)
│ │ ├── train (processed)
│ │ ├── test (processed)
│ ├── potsdam (the same with vaihingen)
│ │ ├── 2_Ortho_RGB (original images)
│ │ ├── 5_Labels_all (original labels)
│ │ ├── 5_Labels_all_noBoundary (original noBoundary lables)
│ │ ├── train (processed)
│ │ ├── test (processed)
train
python tools/dataset_patch_split.py \
--dataset-type "vaihingen" \
--img-dir "/home/xwma/data/Vaihingen/ISPRS_semantic_labeling_Vaihingen/top" \
--mask-dir "/home/xwma/data/Vaihingen/ISPRS_semantic_labeling_Vaihingen_ground_truth_COMPLETE" \
--output-img-dir "data/vaihingen/train/images_1024" \
--output-mask-dir "data/vaihingen/train/masks_1024" \
--split-size 1024 \
--stride 512 \
--mode "train"test and val
python tools/dataset_patch_split.py \
--dataset-type "vaihingen" \
--img-dir "/home/xwma/data/Vaihingen/ISPRS_semantic_labeling_Vaihingen/top" \
--mask-dir "/home/xwma/data/Vaihingen/ISPRS_semantic_labeling_Vaihingen_ground_truth_COMPLETE" \
--output-img-dir "data/vaihingen/test/images_1024" \
--output-mask-dir "data/vaihingen/test/masks_1024_RGB" \
--split-size 1024 \
--stride 1024 \
--mode "test"python tools/dataset_patch_split.py \
--dataset-type "vaihingen" \
--img-dir "/home/xwma/data/Vaihingen/ISPRS_semantic_labeling_Vaihingen/top" \
--mask-dir "/home/xwma/data/Vaihingen/ISPRS_semantic_labeling_Vaihingen_ground_truth_eroded_COMPLETE" \
--output-img-dir "data/vaihingen/test/images_1024" \
--output-mask-dir "data/vaihingen/test/masks_1024" \
--split-size 1024 \
--stride 1024 \
--mode "test"train
python tools/dataset_patch_split.py \
--dataset-type "potsdam" \
--img-dir "/home/xwma/data/Potsdam/2_Ortho_RGB" \
--mask-dir "/home/xwma/data/Potsdam/5_Labels_all" \
--output-img-dir "data/potsdam/train/images_1024" \
--output-mask-dir "data/potsdam/train/masks_1024" \
--split-size 1024 \
--stride 512 \
--mode "train"test and val
python tools/dataset_patch_split.py \
--dataset-type "potsdam" \
--img-dir "/home/xwma/data/Potsdam/2_Ortho_RGB" \
--mask-dir "/home/xwma/data/Potsdam/5_Labels_all_noBoundary" \
--output-img-dir "data/potsdam/test/images_1024" \
--output-mask-dir "data/potsdam/test/masks_1024" \
--split-size 1024 \
--stride 1024 \
--mode "test"python tools/dataset_patch_split.py \
--dataset-type "potsdam" \
--img-dir "/home/xwma/data/Potsdam/2_Ortho_RGB" \
--mask-dir "/home/xwma/data/Potsdam/5_Labels_all" \
--output-img-dir "data/potsdam/test/images_1024" \
--output-mask-dir "data/potsdam/test/masks_1024_RGB" \
--split-size 1024 \
--stride 1024 \
--mode "test"python train.py -c "configs/logcan.py"Vaihingen and Potsdam
python test.py \
-c "configs/logcan.py" \
--ckpt "work_dirs/LoGCAN_ResNet50_Loveda/epoch=45.ckpt" \LoveDA Note that since the loveda dataset needs to be evaluated online, we provide the corresponding test commands.
python online_test.py \
-c "configs/logcan.py" \
--ckpt "work_dirs/LoGCAN_ResNet50_Loveda/epoch=45.ckpt" \We provide two useful commands to test the model for parameters, flops and latency.
python tools/flops_params_count.py \
-c "configs/logcan.py" \python tools/latency_count.py \
-c "configs/logcan.py" \
--ckpt "work_dirs/LoGCAN_ResNet50_Loveda/epoch=45.ckpt" \We will support feature visualizations as well as attention relationship visualizations soon.
If you find our repo useful for your research, please consider giving a 🌟 and citing our work below.
@inproceedings{logcan,
title={Log-can: local-global class-aware network for semantic segmentation of remote sensing images},
author={Ma, Xiaowen and Ma, Mengting and Hu, Chenlu and Song, Zhiyuan and Zhao, Ziyan and Feng, Tian and Zhang, Wei},
booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={1--5},
year={2023},
organization={IEEE}
}
@inproceedings{sacanet,
title={Sacanet: scene-aware class attention network for semantic segmentation of remote sensing images},
author={Ma, Xiaowen and Che, Rui and Hong, Tingfeng and Ma, Mengting and Zhao, Ziyan and Feng, Tian and Zhang, Wei},
booktitle={2023 IEEE International Conference on Multimedia and Expo (ICME)},
pages={828--833},
year={2023},
organization={IEEE}
}
@article{docnet,
title={DOCNet: Dual-Domain Optimized Class-Aware Network for Remote Sensing Image Segmentation},
author={Ma, Xiaowen and Che, Rui and Wang, Xinyu and Ma, Mengting and Wu, Sensen and Feng, Tian and Zhang, Wei},
journal={IEEE Geoscience and Remote Sensing Letters},
year={2024},
publisher={IEEE}
}
@ARTICLE{logcan++,
author={Ma, Xiaowen and Lian, Rongrong and Wu, Zhenkai and Guo, Hongbo and Yang, Fan and Ma, Mengting and Wu, Sensen and Du, Zhenhong and Zhang, Wei and Song, Siyang},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={LOGCAN++: Adaptive Local-Global Class-Aware Network For Semantic Segmentation of Remote Sensing Images},
year={2025},
volume={},
number={},
pages={1-1},
doi={10.1109/TGRS.2025.3541871}}
@article{scsm,
title = {A novel scene coupling semantic mask network for remote sensing image segmentation},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {221},
pages = {44-63},
year = {2025},
issn = {0924-2716},
doi = {https://doi.org/10.1016/j.isprsjprs.2025.01.025},
url = {https://www.sciencedirect.com/science/article/pii/S0924271625000255},
author = {Xiaowen Ma and Rongrong Lian and Zhenkai Wu and Renxiang Guan and Tingfeng Hong and Mengjiao Zhao and Mengting Ma and Jiangtao Nie and Zhenhong Du and Siyang Song and Wei Zhang},
}
If you are confused about the content of our paper or look forward to further academic exchanges and cooperation, please do not hesitate to contact us. The e-mail address is [email protected]. We look forward to hearing from you!
Thanks to previous open-sourced repo: