zjut-multimediaplus/ms-gogo

Mine Diversified Contents of Multispectral Cloud Images Along With Geographical Information for Multilabel Classification
Dongxiaoyuan Zhao; Qiong Wang; Jinglin Zhang; Cong Bai
IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-15, 2023, Art no. 4102415, doi: 10.1109/TGRS.2023.3270204. [paper link]

download data

[click to download LSCIDMR-V2]

fetch code: 9999

Environment Prepare

pytorch version

torchvision version: torchvision-0.11.0%2Bcu111-cp38-cp38-linux_x86_64.whl
torch version: torch-1.10.0%2Bcu111-cp38-cp38-linux_x86_64.whl

other dependencies

pip install requirements.txt

inference model

1. dowload trained model [click to download trained model]

   fetch code: 9999

2. move the downloaded folder/final_model into /MS_GoGo/results

3. run inference

   python main.py --dataset=LSCIDMR_16c\
   --model=ms_gogo\
   --backbone=cnn\
   --inference\
   --use_loc\
   --use_month\ 
   --optim=adam\
   --saved_model_name=final_model\
   --layers=2
   

train new model

run training

python main.py --model=ms_gogo --batch_size=64 --use_month --use_loc

Citing

@ARTICLE{ms_gogo,
  author={Zhao, Dongxiaoyuan and Wang, Qiong and Zhang, Jinglin and Bai, Cong},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Mine Diversified Contents of Multispectral Cloud Images Along With Geographical Information for Multilabel Classification}, 
  year={2023},
  volume={61},
  number={},
  pages={1-15},
  doi={10.1109/TGRS.2023.3270204}
}