SSESN

The official PyTorch implementation of our JSTARS 2022 paper:

Spatially and Semantically Enhanced Siamese Network for Semantic Change Detection in High Resolution Remote Sensing Images

Release

Trained Models (including both bcd and scd models)

Getting Started

Environment

Our experiments are conducted with python3.6, pytorch1.0.0, and CUDA 10.0.

Install the requirements using pip install -r requirements.txt

Data Preparation

-dataset
	|-SECOND
		|-train
			|-im1
			|-im2
			|-label1			 	(label1_gray to rgb for visualization)
			|-label2
			|-label1_gray		(label with 0, 1, 2, ..., 6)
			|-label2_gray
			|-mask0_1				(binary mask)
		|-test
			|...					 	(same with train)
	|-CDD
		|-subset
			|-train
				|-A
				|-B
				|-OUT
			|-test
				|...
			|-val
				|...

Training

python train_xx.py --checkpointdir ... --datadir ...

Testing & Visualization
```
python visualization.py
```
Evaluation
```
python SCDD_eval.py
```

Citation

If any parts of our paper and code help your research, please consider citing us and giving a star to our repository.

article{zhao2022spatially,
  title={Spatially and semantically enhanced siamese network for semantic change detection in high-resolution remote sensing images},
  author={Zhao, Manqi and Zhao, Zifei and Gong, Shuai and Liu, Yunfei and Yang, Jian and Xiong, Xiong and Li, Shengyang},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  volume={15},
  pages={2563--2573},
  year={2022},
  publisher={IEEE}
}

Contact

If you have any questions or concerns, feel free to open issues or contact me through email [zhaomanqi19@csu.ac.cn].

DonDominic/SSESN