Commonality Feature Representation Learning for Unsupervised Multimodal Change Detection

The main challenge of multimodal change detection (MCD) is that multimodal bitemporal images (MBIs) cannot be compared directly to identify changes. To overcome this problem, this paper proposes a novel commonality feature representation learning (CFRL) and constructs a CFRL-based unsupervised MCD f...

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Bibliographic Details
Published in:	IEEE transactions on image processing Vol. 34; pp. 1219 - 1233
Main Authors:	Liu, Tongfei, Zhang, Mingyang, Gong, Maoguo, Zhang, Qingfu, Jiang, Fenlong, Zheng, Hanhong, Lu, Di
Format:	Journal Article
Language:	English
Published:	United States IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Autoencoders Change detection Clustering Clustering algorithms Coders Commonality commonality feature Data mining Decoders Decoding Electronic mail Feature extraction heterogeneous images Image reconstruction Image sensors Learning Multimodal change detection Representation learning Representations Training unsupervised change detection
ISSN:	1057-7149, 1941-0042, 1941-0042
Online Access:	Get full text
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