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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

Viet Khanh Ha

Jinchang Ren

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 IEEE Institute of Electrical and Electronics Engineers
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

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