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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