SmallBambooCode 修改版为了更加方便的训练&测试

增加了测试日志保存
修改了部分数据集文件的默认地址
增加shell脚本以更方便的训练&测试模型

ConvLSR-Net

This is the code for our paper:

LSRFormer: Efficient Transformer Supply Convolutional Neural Networks with Global Information for Aerial Image Segmentation in IEEE Transactions on Geoscience and Remote Sensing 2024.

Results

We repeated the experiment with 5 different random seeds. The average and best results of the 5 repetitions are as follows:

Method	Dataset	mIoU (Average)	mIoU (Best)
ConvLSR-Net	iSAID	70.8±0.11	70.89
ConvLSR-Net	Vaihingen	84.56±0.06	84.64
ConvLSR-Net	Potsdam	87.80±0.08	87.91
ConvLSR-Net	LoveDA	54.77±0.08	54.86

Due to some random operations in the training stage, reproduced results (run once) may slightly different from the reported in paper.

Data Preprocessing

Please follw the GeoSeg to preprocess the LoveDA, Potsdam and Vaihingen dataset.

Please follow the mmsegmentation to preprocess the iSAID dataset.

Training

"-c" means the path of the config, use different config to train different models.

python train_supervision.py -c ./config/isaid/convlsrnet.py

python train_supervision_dp.py -c ./config/potsdam/convlsrnet.py

python train_supervision_dp.py -c ./config/vaihingen/convlsrnet.py

python train_supervision_dp.py -c ./config/loveda/convlsrnet.py

Testing

iSAID

python test_isaid.py -c ./config/isaid/convlsrnet.py -o ./fig_results/isaid/convlsrnet_isaid/  -t "d4"

Vaihingen

python test_vaihingen.py -c ./config/vaihingen/convlsrnet.py -o ./fig_results/convlsrnet_vaihingen/ --rgb -t "d4"

Potsdam

python test_potsdam.py -c ./config/potsdam/convlsrnet.py -o ./fig_results/convlsrnet_potsdam/ --rgb -t "d4"

LoveDA (Online Testing)

My LoveDA results: LoveDA Test Results

输出RGB图像（线下测试，直接输出mIOU）

python test_loveda.py -c ./config/loveda/convlsrnet.py -o ./fig_results/convlsrnet_loveda_rgb --rgb --val -t "d4"

输出标签图像（在线测试）

python test_loveda.py -c ./config/loveda/convlsrnet.py -o ./fig_results/convlsrnet_loveda_onlinetest -t "d4"

Citation and Contact

If you find this project useful in your research, please consider citing our papers：

R. Zhang, Q. Zhang and G. Zhang, "LSRFormer: Efficient Transformer Supply Convolutional Neural Networks With Global Information for Aerial Image Segmentation," in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-13, 2024, Art no. 5610713, doi: 10.1109/TGRS.2024.3366709.

@ARTICLE{10438484,
  author={Zhang, Renhe and Zhang, Qian and Zhang, Guixu},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={LSRFormer: Efficient Transformer Supply Convolutional Neural Networks With Global Information for Aerial Image Segmentation}, 
  year={2024},
  volume={62},
  number={},
  pages={1-13},
  doi={10.1109/TGRS.2024.3366709}}

If you encounter any problems while running the code, feel free to contact me via stdcoutzrh@gmail.com. Thank you!