Change Detection Repository
In this repository, we provide python implementation of some traditional change detection methods, such as SFA, MAD, some deep learning-based change detection methods, such as SiamCRNN, DSFA, and FCN-based methods, or their original websites. Some multi-temporal data sets are also contained in this repository. We would be very glad if this repository can provide some help to your research in change detection or remote sensing image interpretation.
Change Vector Analysis (CVA)
Change vector analysis (CVA)  is a most commonly used method, which can provide change intensity and change direction.
Slow Feature Analysis (SFA)http://sigma.whu.edu.cn/resource.php.
Multivariate Alteration Detection (MAD)
MAD is a change detection algorithm based on canonical correlation analysis (CCA) that aims to maximize the variance of projection feature difference. For the detailed introduction about MAD, please refer to  and . This reporisty contains the python implementation of MAD. The MATLAB implementation can be founded in http://www.imm.dtu.dk/~alan/software.html.
Deep Learning Methods
Deep Slow Feature Analysis (DSFA)https://github.com/rulixiang/DSFANet or http://sigma.whu.edu.cn/resource.php.
Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network (SiamCRNN)https://github.com/I-Hope-Peace/SiamCRNN.
Deep Kernel PCA Convolutional Mapping Network (KPCA-MNet)https://github.com/I-Hope-Peace/KPCAMNet.
Deep Siamese Multi-scale Convolutional Neural Network
In iterature  and , a multi-scale feature convolution unit (MFCU) is adopted for change detection in multi-temporal VHR images. MFCU can extract multi-scale spatial-spectral features in the same layer. Based on the unit two novel deep siamese convolutional neural networks, called as deep siamese multi-scale convolutional network (DSMS-CN) and deep siamese multi-scale fully convolutional network (DSMS-FCN), are designed for unsupervised and supervised change detection, respectively. Tensorflow implementation of this work can be founded in https://github.com/I-Hope-Peace/DSMSCN.
DCVA  processes pre-change and post-change images through a pre-trained network and extracts bi-temporal deep features for subsequent processing in CD framework. The original Caffe implementation could be founded in https://github.com/sudipansaha/dcvaVHROptical.
Other Change Detection Repository
There also exist some other change detection repositories, you can visit them through below links:
 F. Bovolo and L. Bruzzone, “A Theoretical Framework for Unsupervised Change Detection Based on Change Vector Analysis in the Polar Domain,” IEEE Trans. Geosci. Remote Sens., vol. 45, no. 1, pp. 218–236, 2007.
 C. Wu, B. Du, and L. Zhang, “Slow feature analysis for change detection in multispectral imagery,” IEEE Trans. Geosci. Remote Sens., vol. 52, no. 5, pp. 2858–2874, 2014.
 L. Zhang, C. Wu, and B. Du, “Automatic radiometric normalization for multitemporal remote sensing imagery with iterative slow feature analysis,” IEEE Trans. Geosci. Remote Sens., vol. 52, no. 10, pp. 6141–6155, 2014.
 C. Wu, L. Zhang, and B. Du, “Kernel Slow Feature Analysis for Scene Change Detection,” IEEE Trans. Geosci. Remote Sens., vol. 55, no. 4, pp. 2367–2384, 2017.
 A. A. Nielsen, K. Conradsen, and J. J. Simpson, “Multivariate alteration detection (MAD) and MAF Postprocessing in multispectral, bitemporal image data: New approaches to change detection studies,” Remote Sens. Environ., vol. 64, pp. 1–19, 1998.
 A. A. Nielsen, “The regularized iteratively reweighted MAD method for change detection in multi- and hyperspectral data,” IEEE Trans. Image Process., vol. 16, no. 2, pp. 463–478, 2007.
 B. Du, L. Ru, C. Wu, and L. Zhang, “Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images,” IEEE Trans. Geosci. Remote Sens., vol. 57, no. 12, pp. 9976–9992, 2019.
 H. Chen, C. Wu, B. Du, L. Zhang, and L. Wang, “Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network,” IEEE Trans. Geosci. Remote Sens., vol. 58, no. 4, pp. 2848–2864, 2020.
 C. Wu, H. Chen, B. Do, and L. Zhang, “Unsupervised Change Detection in Multi-temporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network,” arXiv preprint arXiv:1912.08628, 2019. https://arxiv.org/abs/1912.08628v1.
 F. Gao, J. Dong, B. Li, and Q. Xu, “Automatic Change Detection in Synthetic Aperture Radar Images Based on PCANet,” IEEE Geosci. Remote Sens. Lett., vol. 13, no. 12, pp. 1792–1796, 2016.
 T. H. Chan, K. Jia, S. Gao, J. Lu, Z. Zeng, and Y. Ma, “PCANet: A Simple Deep Learning Baseline for Image Classification?,” IEEE Trans. Image Process., vol. 24, no. 12, pp. 5017–5032, 2015.
 T. Celik, “Unsupervised change detection in satellite images using principal component analysis and K-means clustering,” IEEE Geosci. Remote Sens. Lett., vol. 6, no. 4, pp. 772–776, 2009.
 M. Zhang and W. Shi, “A Feature Difference Convolutional Neural Network-Based Change Detection Method,” IEEE Trans. Geosci. Remote Sens., vol. 58, no. 10, pp. 7232–7246, 2020.  H. Chen, C. Wu, B. Du and L. Zhang, "Deep Siamese Multi-scale Convolutional Network for Change Detection in Multi-temporal VHR Images," 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), Shanghai, China, 2019, pp. 1-4.
 H. Chen, C. Wu, B. Du and L. Zhang, "Change Detection in Multi-temporal VHR Images Based on Deep Siamese Multi-scale Convolutional Neural Network," arXiv preprint arXiv:1912.08628, 2020. https://arxiv.org/abs/1906.11479.
 S. Saha, F. Bovolo, and L. Bruzzone, “Unsupervised deep change vector analysis for multiple-change detection in VHR Images,” IEEE Trans. Geosci. Remote Sens., vol. 57, no. 6, pp. 3677–3693, 2019.
 L. Ru, B. Du and C. Wu, "Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion," in IEEE Transactions on Image Processing, doi: 10.1109/TIP.2020.3039328.
 S. Fang, K. Li, J. Shao and Z. Li, "SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images," in IEEE Geoscience and Remote Sensing Letters, doi: 10.1109/LGRS.2021.3056416.
Q & A
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