{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T09:37:51Z","timestamp":1770457071478,"version":"3.49.0"},"reference-count":37,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T00:00:00Z","timestamp":1623974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61901362"],"award-info":[{"award-number":["61901362"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2019JQ-729"],"award-info":[{"award-number":["2019JQ-729"]}]},{"name":"&quot;Chunhui Plan&quot; of the Ministry of Education of China","award":["112-425920021"],"award-info":[{"award-number":["112-425920021"]}]},{"name":"PhD research startup foundation of Xi\u2019an University of Technology","award":["112\/256081809"],"award-info":[{"award-number":["112\/256081809"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral image (HSI) super-resolution has gained great attention in remote sensing, due to its effectiveness in enhancing the spatial information of the HSI while preserving the high spectral discriminative ability, without modifying the imagery hardware. In this paper, we proposed a novel HSI super-resolution method via a gradient-guided residual dense network (G-RDN), in which the spatial gradient is exploited to guide the super-resolution process. Specifically, there are three modules in the super-resolving process. Firstly, the spatial mapping between the low-resolution HSI and the desired high-resolution HSI is learned via a residual dense network. The residual dense network is used to fully exploit the hierarchical features learned from all the convolutional layers. Meanwhile, the gradient detail is extracted via a residual network (ResNet), which is further utilized to guide the super-resolution process. Finally, an empirical weight is set between the fully obtained global hierarchical features and the gradient details. Experimental results and the data analysis on three benchmark datasets with different scaling factors demonstrated that our proposed G-RDN achieved favorable performance.<\/jats:p>","DOI":"10.3390\/rs13122382","type":"journal-article","created":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T11:19:20Z","timestamp":1624015160000},"page":"2382","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Hyperspectral Image Super-Resolution under the Guidance of Deep Gradient Information"],"prefix":"10.3390","volume":"13","author":[{"given":"Minghua","family":"Zhao","sequence":"first","affiliation":[{"name":"The Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"given":"Jiawei","family":"Ning","sequence":"additional","affiliation":[{"name":"The Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"given":"Jing","family":"Hu","sequence":"additional","affiliation":[{"name":"The Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"given":"Tingting","family":"Li","sequence":"additional","affiliation":[{"name":"The Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","article-title":"Hyperspectral Remote Sensing Data Analysis and Future Challenges","volume":"1","author":"Plaza","year":"2013","journal-title":"IEEE Geosci. 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