{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T07:21:56Z","timestamp":1770880916560,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,31]],"date-time":"2023-12-31T00:00:00Z","timestamp":1703980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2022YFF1300201"],"award-info":[{"award-number":["2022YFF1300201"]}]},{"name":"National Key Research and Development Program of China","award":["42101380"],"award-info":[{"award-number":["42101380"]}]},{"name":"National Natural Science Foundation of China","award":["2022YFF1300201"],"award-info":[{"award-number":["2022YFF1300201"]}]},{"name":"National Natural Science Foundation of China","award":["42101380"],"award-info":[{"award-number":["42101380"]}]},{"name":"China Scholarship Council","award":["2022YFF1300201"],"award-info":[{"award-number":["2022YFF1300201"]}]},{"name":"China Scholarship Council","award":["42101380"],"award-info":[{"award-number":["42101380"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral images (HSIs) are widely used to identify and characterize objects in scenes of interest, but they are associated with high acquisition costs and low spatial resolutions. With the development of deep learning, HSI reconstruction from low-cost and high-spatial-resolution RGB images has attracted widespread attention. It is an inexpensive way to obtain HSIs via the spectral reconstruction (SR) of RGB data. However, due to a lack of consideration of outdoor solar illumination variation in existing reconstruction methods, the accuracy of outdoor SR remains limited. In this paper, we present an attention neural network based on an adaptive weighted attention network (AWAN), which considers outdoor solar illumination variation by prior illumination information being introduced into the network through a basic 2D block. To verify our network, we conduct experiments on our Variational Illumination Hyperspectral (VIHS) dataset, which is composed of natural HSIs and corresponding RGB and illumination data. The raw HSIs are taken on a portable HS camera, and RGB images are resampled directly from the corresponding HSIs, which are not affected by illumination under CIE-1964 Standard Illuminant. Illumination data are acquired with an outdoor illumination measuring device (IMD). Compared to other methods and the reconstructed results not considering solar illumination variation, our reconstruction results have higher accuracy and perform well in similarity evaluations and classifications using supervised and unsupervised methods.<\/jats:p>","DOI":"10.3390\/rs16010180","type":"journal-article","created":{"date-parts":[[2023,12,31]],"date-time":"2023-12-31T10:01:06Z","timestamp":1704016866000},"page":"180","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Attention Network with Outdoor Illumination Variation Prior for Spectral Reconstruction from RGB Images"],"prefix":"10.3390","volume":"16","author":[{"given":"Liyao","family":"Song","sequence":"first","affiliation":[{"name":"Institute of Artificial Intelligence and Data Science, Xi\u2019an Technological University, Xi\u2019an 710021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0914-8170","authenticated-orcid":false,"given":"Haiwei","family":"Li","sequence":"additional","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics of Chinese Academy of Sciences, Xi\u2019an 710119, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1061-5274","authenticated-orcid":false,"given":"Song","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Measuring and Optical Engineering, Nanchang Hangkong University, Nanchang 330063, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics of Chinese Academy of Sciences, Xi\u2019an 710119, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiancun","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Information and Communications Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quan","family":"Wang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics of Chinese Academy of Sciences, Xi\u2019an 710119, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4817-2875","authenticated-orcid":false,"given":"Jocelyn","family":"Chanussot","sequence":"additional","affiliation":[{"name":"GIPSA-Lab, CNRS, Grenoble INP, Universit\u00e9 Grenoble Alpes, 38000 Grenoble, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hu, X., Xie, C., Fan, Z., Duan, Q., Zhang, D., Jiang, L., and Chanussot, J. 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