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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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 60; S. 1 - 14
Hauptverfasser:	Gao, Lianru, Han, Zhu, Hong, Danfeng, Zhang, Bing, Chanussot, Jocelyn
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Schlagworte:	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
Online-Zugang:	Volltext
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