Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images

Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images

Image translation with convolutional autoencoders has recently been used as an approach to multimodal change detection (CD) in bitemporal satellite images. A main challenge is the alignment of the code spaces by reducing the contribution of change pixels to the learning of the translation function....

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Bibliographic Details
Main Authors: Anfinsen, S.N (Author), Bianchi, F.M (Author), Hansen, M.A (Author), Jenssen, R. (Author), Kampffmeyer, M. (Author), Luppino, L.T (Author), Moser, G. (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2022
Subjects:
Affinity matrix
Aligned autoencoder
aligned autoencoders
Alignment
Auto encoders
Code
Codes
Codes (symbols)
Decoding
deep learning
Deep learning
heterogeneous data
Heterogeneous data
image regression
Image regression
Image-analysis
Learning algorithms
Multimodal image analyse
multimodal image analysis
Multimodal images
Pixels
Remote sensing
Remote-sensing
Space optics
Space-based radar
Synthetic aperture radar
Tensors
Training
Unsupervised change detection
unsupervised change detection (CD).
Unsupervised change detection .
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