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Unsupervised Change Detection in Multitemporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network
- Source :
- IEEE Transactions on Cybernetics. 52:12084-12098
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
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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.
- Subjects :
- FOS: Computer and information sciences
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Feature extraction
Computer Science - Computer Vision and Pattern Recognition
Kernel principal component analysis
FOS: Electrical engineering, electronic engineering, information engineering
Electrical and Electronic Engineering
Cluster analysis
Principal Component Analysis
business.industry
Deep learning
Image and Video Processing (eess.IV)
Pattern recognition
Electrical Engineering and Systems Science - Image and Video Processing
Computer Science Applications
Human-Computer Interaction
Kernel (image processing)
Control and Systems Engineering
Feature (computer vision)
Principal component analysis
Artificial intelligence
business
Algorithms
Software
Change detection
Information Systems
Subjects
Details
- ISSN :
- 21682275 and 21682267
- Volume :
- 52
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Cybernetics
- Accession number :
- edsair.doi.dedup.....1839434c8cd54e782569da55ac57edcd