{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T23:57:52Z","timestamp":1767830272509,"version":"3.49.0"},"reference-count":37,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T00:00:00Z","timestamp":1606176000000},"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":["61860130"],"award-info":[{"award-number":["61860130"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41674144"],"award-info":[{"award-number":["41674144"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Department of Jiangxi Water Resources","award":["KT201616"],"award-info":[{"award-number":["KT201616"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The visualization of near infrared hyperspectral images is valuable for quick view and information survey, whereas methods using band selection or dimension reduction fail to produce good colors as reasonable as corresponding multispectral images. In this paper, an end-to-end neural network of hyperspectral visualization is proposed, based on the convolutional neural networks, to transform a hyperspectral image of hundreds of near infrared bands to a three-band image. Supervised learning is used to train the network where multispectral images are targeted to reconstruct naturally looking images. Each pair of the training images shares the same geographic location and similar moments. The generative adversarial framework is used with an adversarial network to improve the training of the generating network. In the experimental procedure, the proposed method is tested for the near infrared bands of EO-1 Hyperion images with LandSat-8 images as the benchmark, which is compared with five state-of-the-art visualization algorithms. The experimental results show that the proposed method performs better in producing naturally looking details and colors for near infrared hyperspectral images.<\/jats:p>","DOI":"10.3390\/rs12233848","type":"journal-article","created":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T09:06:28Z","timestamp":1606208788000},"page":"3848","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Visualizing Near Infrared Hyperspectral Images with Generative Adversarial Networks"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0154-3456","authenticated-orcid":false,"given":"Rongxin","family":"Tang","sequence":"first","affiliation":[{"name":"Institute of Space Science and Technology, Nanchang University, Nanchang 330031, China"},{"name":"Jiangxi Provincial Key Laboratory of Interdisciplinary Science, Nanchang University, Nanchang 330031, China"}]},{"given":"Hualin","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Space Science and Technology, Nanchang University, Nanchang 330031, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1621-4674","authenticated-orcid":false,"given":"Jingbo","family":"Wei","sequence":"additional","affiliation":[{"name":"Institute of Space Science and Technology, Nanchang University, Nanchang 330031, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2647","DOI":"10.1109\/JSTARS.2013.2272654","article-title":"Hyperspectral Image Visualization Using Band Selection","volume":"7","author":"Su","year":"2014","journal-title":"IEEE J. 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