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iLearn-Lab/
TGRS24-HFCNet

salient-object-detection

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

Yutong Liu¹ Mingzhu Xu¹ Tianxiang Xiao¹ Haoyu Tang¹ Yupeng Hu^1✉ Liqiang Nie¹

¹Affiliation (Please update if needed)

Official implementation of HFCNet, a Heterogeneous Feature Collaboration Network for Salient Object Detection (SOD) in Optical Remote Sensing Images.

🔗 Journal: IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2024
🔗 Task: Salient Object Detection (SOD)
🔗 Framework: PyTorch

📌 Model Information

1. Model Name

HFCNet (Heterogeneous Feature Collaboration Network)

2. Task Type & Applicable Tasks

Task Type: Salient Object Detection / Remote Sensing
Core Task: Salient object detection in optical remote sensing imagery
Applicable Scenarios:
- Remote sensing scene understanding
- Aerial object detection
- Environmental monitoring

3. Project Introduction

Salient Object Detection (SOD) in remote sensing images is challenging due to complex backgrounds, scale variations, and heterogeneous feature distributions.

HFCNet proposes a Heterogeneous Feature Collaboration framework, which:

Integrates multi-level heterogeneous features
Enhances feature interaction and collaboration
Improves representation of salient objects across scales
Strengthens robustness against background interference

4. Training Data Source

Supported datasets:

ORSSD
EORSSD
ORSI

🚀 Pre-trained Weights

Initialization Weights

Download backbone weights:

Swin Transformer
VGG16

Place .pth files into:./pretrained

Trained Weights

Download trained model weights:

Baidu Cloud: https://pan.baidu.com/s/1bVC4uxf3xKhLRcC08EQKMQ?pwd=hfcn

🚀 Training

Download datasets and pre-trained weights
Prepare dataset path lists (.txt files)
Update dataset paths in config files

Run training:

bash

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 &

## 🚀 Testing
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

## ⚠️ Notes
Designed for academic research
Performance depends on dataset characteristics
Requires GPU for efficient training

## 📝Citation
@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},
  pages={1-14}
}

iLearn-Lab/TGRS24-HFCNet