Commonality Autoencoder: Learning Common Features for Change Detection From Heterogeneous Images

Change detection based on heterogeneous images, such as optical images and synthetic aperture radar images, is a challenging problem because of their huge appearance differences. To combat this problem, we propose an unsupervised change detection method that contains only a convolutional autoencoder...

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Bibliographic Details
Published in:	IEEE transaction on neural networks and learning systems Vol. 33; no. 9; pp. 4257 - 4270
Main Authors:	Wu, Yue, Li, Jiaheng, Yuan, Yongzhe, Qin, A. K., Miao, Qi-Guang, Gong, Mao-Guo
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.09.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Change detection Commonality commonality autoencoder convolutional autoencoder (CAE) Decoding deep neural networks (DNNs) Feature extraction Image segmentation Optical imaging Optical sensors Radar imaging Radar polarimetry Remote sensing Representations Synthetic aperture radar
ISSN:	2162-237X, 2162-2388, 2162-2388
Online Access:	Get full text
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