Enhanced Autoencoders With Attention-Embedded Degradation Learning for Unsupervised Hyperspectral Image Super-Resolution

Recently, unmixing-based networks have shown significant potential in unsupervised multispectral-aided hyperspectral image super-resolution (MS-aided HS-SR) task. Nevertheless, the representation ability of unsupervised networks and the design of loss functions still have not been fully explored, le...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 61; pp. 1 - 17
Main Authors: Gao, Lianru, Li, Jiaxin, Zheng, Ke, Jia, Xiuping
Format: Journal Article
Language:English
Published: New York IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation models
Coders
Degradation
Feature extraction
Hyperspectral image (HSI)
Hyperspectral imaging
Image enhancement
Image resolution
Learning
Mathematical models
Parameters
Representations
Resolution
Rivers
spectral unmixing
super-resolution
Superresolution
Task analysis
Tensors
Training
unsupervised learning
ISSN:0196-2892, 1558-0644
Online Access:Get full text
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Summary:Recently, unmixing-based networks have shown significant potential in unsupervised multispectral-aided hyperspectral image super-resolution (MS-aided HS-SR) task. Nevertheless, the representation ability of unsupervised networks and the design of loss functions still have not been fully explored, leaving large room for further improvement. To this end, we propose an enhanced unmixing-inspired unsupervised network with attention-embedded degradation learning (EU2ADL) to realize MS-aided HS-SR. First, two coupled autoencoders serve as the backbone of EU2ADL network to simultaneously decompose input modalities into abundances and corresponding endmembers, whose encoder part is composed of a spatial-spectral two-stream subnetwork for modality-salient representation learning and a parameter-shared one-stream subnetwork for modality-interacted representation enhancement. More importantly, a hybrid model-constrained loss containing a perceptual abundance term and a degradation-guided term is introduced to further eliminate the latent distortions. Since the hybrid loss is built on the degradation model, we additionally present an attention-embedded degradation learning network to adaptively estimate the unknown degradation parameters. Extensive experimental results on four datasets demonstrate the effectiveness of our proposed methods when compared with state of the arts.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2023.3267890