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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Vydané v:	IEEE transactions on image processing Ročník 34; s. 1219 - 1233
Hlavní autori:	Liu, Tongfei, Zhang, Mingyang, Gong, Maoguo, Zhang, Qingfu, Jiang, Fenlong, Zheng, Hanhong, Lu, Di
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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