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license: apache-2.0
tags:
- salient-object-detection
- remote-sensing
- computer-vision
- multimodal
- pytorch
---
<a id="top"></a>
<div align="center">
<h1>🚀 HFCNet: Heterogeneous Feature Collaboration Network for Salient Object Detection in Optical Remote Sensing Images</h1>
<p>
<b>Yutong Liu</b><sup>1</sup>
<b>Mingzhu Xu</b><sup>1</sup>
<b>Tianxiang Xiao</b><sup>1</sup>
<b>Haoyu Tang</b><sup>1</sup>
<b>Yupeng Hu</b><sup>1✉</sup>
<b>Liqiang Nie</b><sup>1</sup>
</p>
<p>
<sup>1</sup>Affiliation (Please update if needed)
</p>
</div>
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
1. Download datasets and pre-trained weights
2. Prepare dataset path lists (.txt files)
3. 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}
} |