@article { , title = {Multi-scale spatial fusion and regularization induced unsupervised auxiliary task CNN model for deep super-resolution of hyperspectral image.}, 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.}, doi = {10.1109/jstars.2022.3176969}, eissn = {2151-1535}, issn = {1939-1404}, journal = {IEEE journal of selected topics in applied earth observations and remote sensing}, note = {INFO COMPLETE (Now published, checked and updated 4/7/2022 LM; Info of Early Access via IEEExplore alert 26/5/2022 LM) PERMISSION GRANTED (version = VOR; embargo = none; licence = BY; SHERPA = https://v2.sherpa.ac.uk/id/publication/3566 ) DOCUMENT READY (VOR downloaded 4/7/2022 ) ADDITIONAL INFO - Contact: Jingchang Ren}, pages = {4583-4598}, publicationstatus = {Published}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, url = {https://rgu-repository.worktribe.com/output/1674656}, volume = {15}, keyword = {Environment, Energy & Sustainability, Hyperspectral image (HSI), Super-resolution (SR), Multi-scale spatial fusion, Auxiliary task, Convolutional neural networks (CNN)}, year = {2022}, author = {Ha, Viet Khanh and Ren, Jinchang and Wang, Zheng and Genyun, Sun and Zhao, Huimin and Marshall, Stephen} }