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However, single hyperspectral (HS) image SR remains challenging, due to the high spectral dimensionality and lack of available high-resolution information of auxiliary sources. To fully exploit the spectral and spatial characteristics, in this paper, a novel single HS image SR approach is proposed based on a spatial correlation-regularized unmixing convolutional neural network (CNN). The proposed approach takes advantage of a CNN to explore the collaborative spatial and spectral information of an HS image and infer the high-resolution abundance maps, thereby reconstructing the anticipated high-resolution HS image via the linear spectral mixture model. Moreover, a dual-branch architecture network and spatial spread transform function are employed to characterize the spatial correlation between the high- and low-resolution HS images, aiming at promoting the fidelity of the super-resolved image. Experiments on three public remote sensing HS images demonstrate the feasibility and superiority in terms of spectral fidelity, compared with some state-of-the-art HS image super-resolution methods.<\/jats:p>","DOI":"10.3390\/rs13204074","type":"journal-article","created":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T06:38:41Z","timestamp":1634107121000},"page":"4074","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Hyperspectral Image Super-Resolution Based on Spatial Correlation-Regularized Unmixing Convolutional Neural Network"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1745-4992","authenticated-orcid":false,"given":"Xiaochen","family":"Lu","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Donghua University, Shanghai 201620, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4742-7960","authenticated-orcid":false,"given":"Dezheng","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Donghua University, Shanghai 201620, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1082-114X","authenticated-orcid":false,"given":"Junping","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4870-8473","authenticated-orcid":false,"given":"Fengde","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Donghua University, Shanghai 201620, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3008","DOI":"10.1109\/JSTARS.2015.2440092","article-title":"Hyper-sharpening: A first approach on sim-ga data","volume":"8","author":"Selva","year":"2015","journal-title":"IEEE J. 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