CCTNet: Coupled CNN and Transformer Network for Crop Segmentation of Remote Sensing Images, RemoteSensing

Introduction

We propose a Coupled CNN and Transformer Network to combine the local modeling advantage of the CNN and the global modeling advantage of Transformer to achieve SOTA performance on the Barley Remote Sensing Dataset. By applying our code base, you can easily deal with ultra-high-resolution remote sensing images. If our work is helpful to you, please star us.

Usage

• Install packages

  This repository is based on python 3.6.12 and torch 1.6.0.

  git clone https://github.com/zyxu1996/CCTNet.git
  cd CCTNet  
  

  pip install -r requirements.txt
  

• Prepare datasets and pretrained weights

  • The code base has supported three high-resolution datasets, are respective Barley, Potsdam and Vaihingen.

  • Download Barley, Potsdam and Vaihingen datasets form BaiduYun, and put them on ./data
BaiduYun: https://pan.baidu.com/s/1MyDw1qncPKYJFK_zjFxFBA
    Password: s7f2

    Data file structure of the above three datasets is as followed.

    ├── data                    ├── data                      ├── data
        ├──barley                   ├──potsdam                    ├──vaihingen
           ├──images                   ├──images                     ├──images
              ├──image_1_0_0.png          ├──top_potsdam_2_10.tif       ├──top_mosaic_09cm_area1.tif
              ├──image_1_0_1.png          ├──top_potsdam_2_11.tif       ├──top_mosaic_09cm_area2.tif
                 ...                         ...                           ...
           ├──labels                   ├──labels                     ├──labels
              ├──image_1_0_0.png          ├──top_potsdam_2_10.png       ├──top_mosaic_09cm_area1.png
              ├──image_1_0_1.png          ├──top_potsdam_2_11.png       ├──top_mosaic_09cm_area2.png
                 ...                         ...                           ...
           ├──annotations              ├──annotations                ├──annotations
              ├──train.txt                ├──train.txt                  ├──train.txt
              ├──test.txt                 ├──test.txt                   ├──test.txt
    
    

  • Download the pretained weights from CSwin-Transformer, and put them on ./pretrained_weights
    CSwin: CSwin Tiny, Small, Base and Large pretrained on ImageNet-1K and ImageNet-22K are used.
    ResNet: ResNet 18, 34, 50 and 101 pretrained models are used, the download link is contained in the our code.

• Training

  • The training and testing settings are written in the script, including the selection of datasets and models.

    sh autorun.sh
    

  • If directly run train.py, please undo the following code.

    if __name__ == '__main__':
      os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
      os.environ.setdefault('RANK', '0')
      os.environ.setdefault('WORLD_SIZE', '1')
      os.environ.setdefault('MASTER_ADDR', '127.0.0.1')
      os.environ.setdefault('MASTER_PORT', '29556')
    

• Testing

  • Generating the final results and visulizing the prediction.

    cd ./work_dir/your_work
    

  • Do remember undo the test command in sh autorun.sh. And keep the --information num1 in testing command is same as the information in training command.
    CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --nproc_per_node=1 --master_port 29505 test.py --dataset barley --val_batchsize 8 --models cctnet --head seghead --crop_size 512 512 --trans_cnn cswin_tiny resnet50 --save_dir work_dir --base_dir ../../ --information num1
  • Then run the script autorun.sh.

    sh autorun.sh
    

Acknowledgments

Thanks Guangzhou Jingwei Information Technology Co., Ltd., and the Xingren City government for providing the Barley Remote Sensing Dataset. Thanks the ISPRS for providing the Potsdam and Vaihingen datasets.

Citation

@article{wang2022cctnet,
  title={CCTNet: Coupled CNN and Transformer Network for Crop Segmentation of Remote Sensing Images},
  author={Wang, Hong and Chen, Xianzhong and Zhang, Tianxiang and Xu, Zhiyong and Li, Jiangyun},
  journal={Remote Sensing},
  volume={14},
  number={9},
  pages={1956},
  year={2022},
  publisher={MDPI}
}

Other Links

• HRCNet: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images
• Efficient Transformer for Remote Sensing Image Segmentation