Unsupervised Contrastive Hashing With Autoencoder Semantic Similarity for Cross-Modal Retrieval in Remote Sensing

In large-scale multimodal remote sensing data archives, the application of cross-modal technology to achieve fast retrieval between different modalities has attracted great attention. In this article, we focus on cross-modal retrieval technology between remote sensing images and text. At present, th...

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Vydané v:	IEEE journal of selected topics in applied earth observations and remote sensing Ročník 18; s. 6047 - 6059
Hlavní autori:	Liu, Na, Wu, Guodong, Huang, Yonggui, Chen, Xi, Li, Qingdu, Wan, Lihong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Binary codes Codes Cross modal retrieval Data mining Datasets Feature extraction hashing Heterogeneity Image reconstruction Image retrieval Learning Machine learning Modal data Modules Remote sensing Representations Retrieval Semantics Similarity Supervised learning Training unsupervised contrastive learning Vectors
ISSN:	1939-1404, 2151-1535
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