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With the development of Earth observation technology, very-high-resolution(VHR) image has become an important data source of change detection. Nowadays,deep learning methods have achieved conspicuous performance in the changedetection of VHR images. Nonetheless, most of the existing change detectionmodels based on deep learning require annotated training samples. In thispaper, a novel unsupervised model called kernel principal component analysis(KPCA) convolution is proposed for extracting representative features frommulti-temporal VHR images. Based on the KPCA convolution, an unsupervised deepsiamese KPCA convolutional mapping network (KPCA-MNet) is designed for binaryand multi-class change detection. In the KPCA-MNet, the high-levelspatial-spectral feature maps are extracted by a deep siamese networkconsisting of weight-shared PCA convolution layers. Then, the changeinformation in the feature difference map is mapped into a 2-D polar domain.Finally, the change detection results are generated by threshold segmentationand clustering algorithms. All procedures of KPCA-MNet does not require labeleddata. The theoretical analysis and experimental results demonstrate thevalidity, robustness, and potential of the proposed method in two binary changedetection data sets and one multi-class change detection data set.

Unsupervised Change Detection in Multi-temporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network