{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T21:35:34Z","timestamp":1777930534374,"version":"3.51.4"},"reference-count":37,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T00:00:00Z","timestamp":1683504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spark funding","award":["HHJJ-2022-0101"],"award-info":[{"award-number":["HHJJ-2022-0101"]}]},{"name":"Spark funding","award":["2022GY-060"],"award-info":[{"award-number":["2022GY-060"]}]},{"name":"Spark funding","award":["XWYCXY-012021019"],"award-info":[{"award-number":["XWYCXY-012021019"]}]},{"name":"General project of the key R&amp;D Plan of Shaanxi Province","award":["HHJJ-2022-0101"],"award-info":[{"award-number":["HHJJ-2022-0101"]}]},{"name":"General project of the key R&amp;D Plan of Shaanxi Province","award":["2022GY-060"],"award-info":[{"award-number":["2022GY-060"]}]},{"name":"General project of the key R&amp;D Plan of Shaanxi Province","award":["XWYCXY-012021019"],"award-info":[{"award-number":["XWYCXY-012021019"]}]},{"name":"Wuhu and Xidian University special fund for industry\u2013university research cooperation","award":["HHJJ-2022-0101"],"award-info":[{"award-number":["HHJJ-2022-0101"]}]},{"name":"Wuhu and Xidian University special fund for industry\u2013university research cooperation","award":["2022GY-060"],"award-info":[{"award-number":["2022GY-060"]}]},{"name":"Wuhu and Xidian University special fund for industry\u2013university research cooperation","award":["XWYCXY-012021019"],"award-info":[{"award-number":["XWYCXY-012021019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral images (HSIs) generally contain tens or even hundreds of spectral segments within a specific frequency range. Due to the limitations and cost of imaging sensors, HSIs often trade spatial resolution for finer band resolution. To compensate for the loss of spatial resolution and maintain a balance between space and spectrum, existing algorithms were used to obtain excellent results. However, these algorithms could not fully mine the coupling relationship between the spectral domain and spatial domain of HSIs. In this study, we presented a spectral correlation and spatial high\u2013low frequency information of a hyperspectral image super-resolution network (SCSFINet) based on the spectrum-guided attention for analyzing the information already obtained from HSIs. The core of our algorithms was the spectral and spatial feature extraction module (SSFM), consisting of two key elements: (a) spectrum-guided attention fusion (SGAF) using SGSA\/SGCA and CFJSF to extract spectral\u2013spatial and spectral\u2013channel joint feature attention, and (b) high- and low-frequency separated multi-level feature fusion (FSMFF) for fusing the multi-level information. In the final stage of upsampling, we proposed the channel grouping and fusion (CGF) module, which can group feature channels and extract and merge features within and between groups to further refine the features and provide finer feature details for sub-pixel convolution. The test on the three general hyperspectral datasets, compared to the existing hyperspectral super-resolution algorithms, suggested the advantage of our method.<\/jats:p>","DOI":"10.3390\/rs15092472","type":"journal-article","created":{"date-parts":[[2023,5,9]],"date-time":"2023-05-09T01:06:28Z","timestamp":1683594388000},"page":"2472","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Spectral Correlation and Spatial High\u2013Low Frequency Information of Hyperspectral Image Super-Resolution Network"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8495-2804","authenticated-orcid":false,"given":"Jing","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Lntegrated Service Network, Xidian University, Xi\u2019an 710071, China"},{"name":"School of Telecommunication Engineering, Xidian University, Xi\u2019an 710071, China"},{"name":"Guangzhou Institute of Technology, Xidian University, Guangzhou 510700, China"},{"name":"Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renjie","family":"Zheng","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Chen","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaolong","family":"Hong","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunsong","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Lntegrated Service Network, Xidian University, Xi\u2019an 710071, China"},{"name":"School of Telecommunication Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruitao","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Control Engineering, Rocket Force University of Engineering, Xi\u2019an 710025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3852","DOI":"10.1109\/JSTARS.2019.2903642","article-title":"Toward Efficient Land Cover Mapping: An Overview of the National Land Representation System and Land Cover Map 2015 of Bangladesh","volume":"12","author":"Jalal","year":"2019","journal-title":"IEEE J. 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