CyCU-Net: Cycle-Consistency Unmixing Network by Learning Cascaded Autoencoders

In recent years, deep learning (DL) has attracted increasing attention in hyperspectral unmixing (HU) applications due to its powerful learning and data fitting ability. The autoencoder (AE) framework, as an unmixing baseline network, achieves good performance in HU by automatically learning low-dim...

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Vydáno v:	IEEE transactions on geoscience and remote sensing Ročník 60; s. 1 - 14
Hlavní autoři:	Gao, Lianru, Han, Zhu, Hong, Danfeng, Zhang, Bing, Chanussot, Jocelyn
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Témata:	Ablation Algorithms Atmospheric modeling Cascaded autoencoders (AEs) Competitiveness Computer Science Consistency cycle consistency Decoding Deep learning deep learning (DL) Feature extraction Hyperspectral imaging hyperspectral unmixing (HU) Image reconstruction Information processing Machine learning Perception Pixels Reconstruction remote sensing (RS) Self image self-perception Semantics Signal and Image Processing Cascaded autoencoders (AEs) self-perception remote sensing (RS) hyperspectral unmixing (HU) cycle consistency deep learning (DL)
ISSN:	0196-2892, 1558-0644
On-line přístup:	Získat plný text
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