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To preserve local submanifold structures in HSI super-resolution, a novel superpixel graph-based super-resolution method is proposed. Firstly, the MSI is segmented into superpixel blocks to form two-directional feature tensors, then two graphs are created using spectral\u2013spatial distance between the unfolded feature tensors. Secondly, two graph Laplacian terms involving underlying BTD factors of high-resolution HSI are developed, which ensures the inheritance of the spatial geometric structures. Finally, by incorporating graph Laplacian priors with the coupled BTD degradation model, a HSI super-resolution model is established. Experimental results demonstrate that the proposed method achieves better fused results compared with other advanced super-resolution methods, especially on the improvement of the spatial structure.<\/jats:p>","DOI":"10.3390\/rs14184520","type":"journal-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T04:05:41Z","timestamp":1663041941000},"page":"4520","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Coupled Tensor Block Term Decomposition with Superpixel-Based Graph Laplacian Regularization for Hyperspectral Super-Resolution"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8941-4346","authenticated-orcid":false,"given":"Hongyi","family":"Liu","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Wen","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Yuchen","family":"Zha","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Zhihui","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,9]]},"reference":[{"key":"ref_1","first-page":"5516222","article-title":"Component Decomposition Analysis for Hyperspectral Anomaly Detection","volume":"60","author":"Chen","year":"2022","journal-title":"IEEE Geosci. 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