Viet Khanh Ha
Multi-scale spatial fusion and regularization induced unsupervised auxiliary task CNN model for deep super-resolution of hyperspectral image.
Ha, Viet Khanh; Ren, Jinchang; Wang, Zheng; Genyun, Sun; Zhao, Huimin; Marshall, Stephen
Authors
Professor Jinchang Ren j.ren@rgu.ac.uk
Professor of Computing Science
Zheng Wang
Sun Genyun
Huimin Zhao
Stephen Marshall
Abstract
Hyperspectral images (HSI) features rich spectral information in many narrow bands but at a cost of a relatively low spatial resolution. As such, various methods have been developed for enhancing the spatial resolution of the low-resolution HSI (Lr-HSI) by fusing it with high-resolution multispectral images (Hr-MSI). The difference in spectrum range and spatial dimensions between the Lr-HSI and Hr-SI have been fundamental but challenging for multispectral/hyperspectral (MS/HS) fusion. In this paper, a multi-scale spatial fusion and regularization induced auxiliary task (MSAT) based CNN model is proposed for deep super-resolution of HSI, where a Lr-HSI is fused with a Hr-MSI to reconstruct a high-resolution HSI (Hr-HSI) counterpart. The multi-scale fusion is used to efficiently address the discrepancy in spatial resolutions between two inputs. Based on the general assumption that the acquired Hr-MSI and the reconstructed Hr-HSI share similar underlying characteristics, the auxiliary task is proposed to learn a representation for improved generality of the model and reduced overfitting. Experimental results on three public datasets have validated the effectiveness of our approach in comparison with several state-of-the-art methods.
Citation
HA, V.K., REN, J., WANG, Z., SUN, G., ZHAO, H. and MARSHALL, S. 2022. Multi-scale spatial fusion and regularization induced unsupervised auxiliary task CNN model for deep super-resolution of hyperspectral image. IEEE journal of selected topics in applied earth observations and remote sensing [online], 15, pages 4583-4598. Available from: https://doi.org/10.1109/JSTARS.2022.3176969
Journal Article Type | Article |
---|---|
Acceptance Date | May 11, 2022 |
Online Publication Date | May 23, 2022 |
Publication Date | Dec 31, 2022 |
Deposit Date | May 26, 2022 |
Publicly Available Date | May 26, 2022 |
Journal | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Print ISSN | 1939-1404 |
Electronic ISSN | 2151-1535 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Peer Reviewed | Peer Reviewed |
Volume | 15 |
Pages | 4583-4598 |
DOI | https://doi.org/10.1109/jstars.2022.3176969 |
Keywords | Hyperspectral image (HSI); Super-resolution (SR); Multi-scale spatial fusion; Auxiliary task; Convolutional neural networks (CNN) |
Public URL | https://rgu-repository.worktribe.com/output/1674656 |
Files
HA 2022 Multi-scale spatial
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Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
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