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HFCNet: Heterogeneous Feature Collaboration Network for Salient Object Detection in Optical Remote Sensing Images

Welcome to the official repository for the paper "Heterogeneous Feature Collaboration Network for Salient Object Detection in Optical Remote Sensing Images", IEEE TGRS, 2024.

The Initialization Weights for Training

Download pre-trained classification weights of the Swin Transformer and VGG, and place the .pth files in ./pretrained directory. These weights are essential for initializing the model during training.

Trained Weights of HFCNet for Testing

Download

Train

Please download the pre-trained model weights and dataset first. Next, generate the path lists of the training set and the test set, and change the dataset path in the code to the path of the dataset listing file (.txt) you specified.

nohup python -u main.py --flag train --model_id HFCNet --config config/dataset_o.yaml --device cuda:0 > train_ORSSD.log &

nohup python -u main.py --flag train --model_id HFCNet --config config/dataset_e.yaml --device cuda:0 > train_EORSSD.log &

nohup python -u main.py --flag train --model_id HFCNet --config config/dataset_orsi.yaml --device cuda:0 > train_ORSI.log &

Test

Download the HFCNet model weights, create the necessary directories to store these files, and be sure to update the corresponding paths in the code accordingly.

mkdir ./modelPTH-ORSSD
python main.py --flag test --model_id HFCNet --config config/dataset_o.yaml

mkdir ./modelPTH-EORSSD
python main.py --flag test --model_id HFCNet --config config/dataset_e.yaml 

mkdir ./modelPTH-ORSI
python main.py --flag test --model_id HFCNet --config config/dataset_orsi.yaml

Citation

If it helps your research, please use the information below to cite our work, thank you.

@ARTICLE{HFCNet,
  author={Liu, Yutong and Xu, Mingzhu and Xiao, Tianxiang and Tang, Haoyu and Hu, Yupeng and Nie, Liqiang},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Heterogeneous Feature Collaboration Network for Salient Object Detection in Optical Remote Sensing Images}, 
  year={2024},
  volume={62},
  number={},
  pages={1-14}}