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<h1 align="center">Exchange means change: an unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange</h1> <h3 align="center"> <a href="https://chrx97.com/">Hongruixuan Chen</a>, <a href="https://github.com/JTRNEO">Jian Song</a>, <a href="https://scholar.google.com/citations?user=DbTt_CcAAAAJ&hl=zh-CN">Chen Wu</a>, <a href="https://scholar.google.com/citations?user=Shy1gnMAAAAJ&hl=zh-CN">Bo Du</a>, and <a href="https://naotoyokoya.com/">Naoto Yokoya</a></h3>

This is an official implementation of I3PE framework in our ISPRS JP&RS 2023 paper: Exchange means change: an unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange.

Get started

Requirements

Please download the following key python packages in advance.

python==3.6.15
pytorch==1.7.0
scikit-learn==0.22.1
scikit-image==0.17.2
imageio=2.15.0
numpy==1.19.5
tqdm==4.64.1

Datasets

Two large-scale benchmark datasets, <a href="https://github.com/liumency/SYSU-CD">SYSU dataset</a> and <a href="https://captain-whu.github.io/SCD/ ">SECOND dataset</a>, are used for experiments. Please download them and organize them in the following way.

For the <strong>Wuhan dataset</strong> used in our paper, you can also download it here for your own research [<a href="https://drive.google.com/file/d/1f9tWouvzwjqf9oujg6BMh-xESzwEefO4/view?usp=drive_link">Google Drive</a>], [<a href="https://pan.baidu.com/s/1XLPPwfLl1HpSo0kzidIDpQ?pwd=8d27">Baidu Cloud</a>].

├── <THE-ROOT-PATH-OF-DATA>/
│   ├── SYSU/     
|   |   ├── train/
|   |   |   ├── T1/
|   |   |   ├── T2/
|   |   |   ├── GT/
|   |   ├── val/
|   |   |   |── ...
|   |   ├── test/
|   |   |   |── ...
|   |   
│   ├── SECOND/     
|   |   ├── train/
|   |   |   |── ...
|   |   ├── test/
|   |   |   |── ...

Generate Single-Temporal Training Sets

Transfer the images in T1 and T2 under original training set to a new folder and rename the images.

python construct_single_temporal_set.py

Generate object maps and cluster maps for intra-image patch exchange method in advance.

python generate_object.py --dataset_path 'your own path here' --obj_num 1000

python generate_clustering_map.py --dataset_path 'your own path here' --eps 7 --min_samples 10

Training Change Detectors

Training the deep change detector on the single-temporal training sets using both intra- and inter-image patch exchangem methods.

python train_network_I3PE.py

Unsupervised change detection results of different methods on the test sets:

	SYSU		SECOND
Method	OA	F1	OA	F1
<a href="https://github.com/ChenHongruixuan/ChangeDetectionRepository/tree/master/Methodology/Traditional/CVA">CVA	0.4539	0.3492	0.4332	0.3003
<a href="https://github.com/ChenHongruixuan/ChangeDetectionRepository/tree/master/Methodology/Traditional/MAD">IRMAD	0.6914	0.3705	0.6829	0.3451
<a href="https://github.com/ChenHongruixuan/ChangeDetectionRepository/tree/master/Methodology/Traditional/SFA"> ISFA	0.6977	0.3695	0.7130	0.3293
OBCD	0.7091	0.4046	0.7005	0.3426
<a href="https://ieeexplore.ieee.org/document/9669957">DCAE	0.7636	0.4390	0.7600	0.3340
<a href="https://github.com/sudipansaha/dcvaVHROptical">DCVA	0.6995	0.4450	0.6795	0.3681
<a href="https://github.com/rulixiang/DSFANet">DSFA</a>	0.6326	0.4125	0.5961	0.3301
<a href="https://github.com/ChenHongruixuan/KPCAMNet">KPCA-MNet</a>	0.7084	0.4482	0.6793	0.3670
I3PE	0.7305	0.5547	0.7283	0.4380

Citation

If this code or dataset contributes to your research, please kindly consider citing our paper :)

@article{Chen2023Exchange,
    title = {Exchange means change: An unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange},
    author = {Hongruixuan Chen and Jian Song and Chen Wu and Bo Du and Naoto Yokoya},
    journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
    volume = {206},
    pages = {87-105},
    year = {2023},
    issn = {0924-2716},
    doi = {https://doi.org/10.1016/j.isprsjprs.2023.11.004}
}

Q & A

For any questions, please contact us.