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This repository contains some python code of some traditional change detection methods or provides their original websites, such as SFA, MAD, and some deep learning-based change detection methods, such as SiamCRNN, DSFA, and some FCN-based methods.
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# 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 datasets](https://github.com/I-Hope-Peace/ChangeDetectionRepository/tree/master/Dataset) 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. ## Traditional Methods ### Change Vector Analysis (CVA) Change vector analysis (CVA) [1] is a most commonly used method, which can provide change intensity and change direction. ### Slow Feature Analysis (SFA) <div align=center><img src="./Figure/SFA.png" width="60%" height="60%"></div> Wu et al. [2] proposed a novel CD method based on slow feature analysis (SFA), which aims to find the most invariant component in multitemporal images to highlight changed regions. In addition to change detection, SFA was also used in radiometric correction [3] and scene change detection [4]. This reporisty contains the Python implementation of SFA and iterative SFA. The MATLAB implementation can be founded in 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 [5] and [6]. This reporisty contains the python implementation of MAD. The MATLAB implementation can be founded in http://www.imm.dtu.dk/~alan/software.html. ### PCA-Kmeans <div align=center><img src="./Figure/PCA_Kmeans.png" width="50%" height="50%"></div> PCA-Kmeans [12] partitones the difference image into nonoverlapping blocks. Orthonormal eigenvectors are extracted through PCA of nonoverlapping block set to create an eigenvector space. Each pixel in the difference image is represented with an S-dimensional feature vector which is the projection difference image data onto the generated eigenvector space. The change detection is achieved by partitioning the feature vector space into two clusters using k-means. ## Deep Learning Methods ### Deep Slow Feature Analysis (DSFA) <div align=center><img src="./Figure/DSFA.png" width="60%" height="60%"></div> DSFA is an unsupervised change detection model that utilizes a dual-stream deep neural network to learn non-linear features and highlights changes via linear SFA. For the detailed introduction about DSFA, please refer to [7]. The Tensorflow implementation of DSFA can be founded in https://github.com/rulixiang/DSFANet or http://sigma.whu.edu.cn/resource.php. ### Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network (SiamCRNN) <div align=center><img src="./Figure/SiamCRNN.png" width="70%" height="70%"></div> SiamCRNN is an end-to-end general multi-source change detection architecture that consists of three subnetworks: deep siamese convolutional neural network (DSCNN), multiple-layers RNN (MRNN), and fully connected (FC) layers. The DSCNN has a flexible structure for multisource image and is able to extract spatial–spectral features from homogeneous or heterogeneous VHR image patches. The MRNN stacked by long-short term memory (LSTM) units is responsible for mapping the spatial–spectral features extracted by DSCNN into a new latent feature space and mining the change information between them. In addition, FC, the last part of SiamCRNN, is adopted to predict change probability. For the detailed introduction about DSFA, please refer to [8]. The Tensorflow implementation of SiamCRNN can be founded in https://github.com/I-Hope-Peace/SiamCRNN. ### Deep Kernel PCA Convolutional Mapping Network (KPCA-MNet) <div align=center><img src="./Figure/KPCAMNet.png" width="70%" height="70%"></div> KPCA-MNet is designed for unsupervised binary and
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