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

Abstract

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 change detection of VHR images. Nonetheless, most of the existing change detection models based on deep learning require annotated training samples. In this paper, a novel unsupervised model called kernel principal component analysis (KPCA) convolution is proposed for extracting representative features from multi-temporal VHR images. Based on the KPCA convolution, an unsupervised deep siamese KPCA convolutional mapping network (KPCA-MNet) is designed for binary and multi-class change detection. In the KPCA-MNet, the high-level spatial-spectral feature maps are extracted by a deep siamese network consisting of weight-shared PCA convolution layers. Then, the change information in the feature difference map is mapped into a 2-D polar domain. Finally, the change detection results are generated by threshold segmentation and clustering algorithms. All procedures of KPCA-MNet does not require labeled data. The theoretical analysis and experimental results demonstrate the validity, robustness, and potential of the proposed method in two binary change detection data sets and one multi-class change detection data set. Index Terms-Change detection, kernel principal component analysis (KPCA), deep siamese KPCA convolutional mapping network, very-high-resolution (VHR) images, unsupervised multiclass change detection I. INTRODUCTIONT HE Earth surface is constantly evolving. Real-time and accurate access to the surface changes is of great importance for the better understanding of human activities, ecosystem and their interactions [1]. Multi-temporal remote sensing images can reveal the dynamic changes in the surface. Therefore, change detection based on multi-temporal images has become more and more significant. Change detection (CD) is the process of identifying differences in the state of an object

Change Detection in VHR Imagery With Severe Co-Registration Errors Using Deep Learning: A Comparative Study

“…Automatically selected features were combined into hypervectors that were compared pixel-wise to obtain deep change vectors for multiclass CD based on the direction of change. Finally, in [36], an unsupervised deep Siamese kernel PCA convolutional mapping network for binary and multiclass CD was designed. The multiclass CD was accomplished by a 2-D polar mapping.…”