{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:24:24Z","timestamp":1777656264709,"version":"3.51.4"},"reference-count":48,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Economic Development of the Russian Federation","award":["139-15-2025-003"],"award-info":[{"award-number":["139-15-2025-003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Hyperspectral remote sensing images (HSIs) provide invaluable information for environmental and agricultural monitoring, yet they are often degraded by atmospheric haze, which distorts spatial and spectral content and hinders downstream analysis. Progress in hyperspectral dehazing has been limited by the absence of paired real-haze benchmarks; most prior studies rely on synthetic haze or unpaired data, restricting fair evaluation and generalization. We present HyperHazeOff, the first comprehensive benchmark for hyperspectral dehazing that unifies data, tasks, and evaluation protocols. It comprises (i) RRealHyperPDID, 110 scenes with paired real-haze and haze-free HSIs (plus RGB images), and (ii) RSyntHyperPDID, 2616 paired samples generated using a physically grounded haze formation model. The benchmark also provides agricultural field delineation and land classification annotations for downstream task quality assessment, standardized train\/validation\/test splits, preprocessing pipelines, baseline implementations, pretrained weights, and evaluation tools. Across six state-of-the-art methods (three RGB-based and three HSI-specific), we find that hyperspectral models trained on the widely used HyperDehazing dataset fail to generalize to real haze, while training on RSyntHyperPDID enables significant real-haze restoration by AACNet. HyperHazeOff establishes reproducible baselines and is openly available to advance research in hyperspectral dehazing.<\/jats:p>","DOI":"10.3390\/jimaging11120422","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T11:43:22Z","timestamp":1764157402000},"page":"422","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["HyperHazeOff: Hyperspectral Remote Sensing Image Dehazing Benchmark"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4292-2049","authenticated-orcid":false,"given":"Artem","family":"Nikonorov","sequence":"first","affiliation":[{"name":"Samara National Research University, Moskovskoye Shosse 34, 443086 Samara, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2873-9387","authenticated-orcid":false,"given":"Dmitry","family":"Sidorchuk","sequence":"additional","affiliation":[{"name":"Institute for Information Transmission Problems, the Russian Academy of Sciences, 127051 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5232-1542","authenticated-orcid":false,"given":"Nikita","family":"Odinets","sequence":"additional","affiliation":[{"name":"Institute for Information Transmission Problems, the Russian Academy of Sciences, 127051 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6058-8764","authenticated-orcid":false,"given":"Vladislav","family":"Volkov","sequence":"additional","affiliation":[{"name":"Federal Research Center Computer Science and Control, the Russian Academy of Sciences, 119333 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0280-1157","authenticated-orcid":false,"given":"Anastasia","family":"Sarycheva","sequence":"additional","affiliation":[{"name":"Institute for Information Transmission Problems, the Russian Academy of Sciences, 127051 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6086-687X","authenticated-orcid":false,"given":"Ekaterina","family":"Dudenko","sequence":"additional","affiliation":[{"name":"Federal Research Center Computer Science and Control, the Russian Academy of Sciences, 119333 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2163-5228","authenticated-orcid":false,"given":"Mikhail","family":"Zhidkov","sequence":"additional","affiliation":[{"name":"Institute for Information Transmission Problems, the Russian Academy of Sciences, 127051 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5560-7668","authenticated-orcid":false,"given":"Dmitry","family":"Nikolaev","sequence":"additional","affiliation":[{"name":"Federal Research Center Computer Science and Control, the Russian Academy of Sciences, 119333 Moscow, Russia"},{"name":"Smart Engines Service LLC, 117312 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, C., Li, Z., Liu, X., and Li, S. (2022). The influence of image degradation on hyperspectral image classification. Remote Sens., 14.","DOI":"10.3390\/rs14205199"},{"key":"ref_2","first-page":"1351709-1","article-title":"TSQ-2024: A Categorized Dataset of 2D LiDAR Images of Moving Dump Trucks in Various Environment Conditions","volume":"Volume 13517","author":"Osten","year":"2025","journal-title":"Proceedings of the Seventeenth International Conference on Machine Vision (ICMV 2024)"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ebel, P., Garnot, V.S.F., Schmitt, M., Wegner, J.D., and Zhu, X.X. (2023, January 17\u201324). UnCRtainTS: Uncertainty quantification for cloud removal in optical satellite time series. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPRW59228.2023.00202"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sarukkai, V., Jain, A., Uzkent, B., and Ermon, S. (2020, January 1\u20135). Cloud removal from satellite images using spatiotemporal generator networks. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093564"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhao, X., and Jia, K. (2023). Cloud removal in remote sensing using sequential-based diffusion models. Remote Sens., 15.","DOI":"10.3390\/rs15112861"},{"key":"ref_6","first-page":"2341","article-title":"Single image haze removal using dark channel prior","volume":"33","author":"He","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1109\/LGRS.2013.2245857","article-title":"Single remote sensing image dehazing","volume":"11","author":"Long","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5250","DOI":"10.1109\/ACCESS.2018.2889766","article-title":"Remote sensing image haze removal using gamma-correction-based dehazing model","volume":"7","author":"Ju","year":"2018","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Luo, Y., Wei, H., Li, Y., Qi, G., Mazur, N., Li, Y., and Li, P. (2021). Atmospheric light estimation based remote sensing image dehazing. Remote Sens., 13.","DOI":"10.3390\/rs13132432"},{"key":"ref_10","first-page":"1307216-1","article-title":"CADCP: A method for chromatic haze compensation on remotely sensed images","volume":"Volume 13072","author":"Osten","year":"2024","journal-title":"Proceedings of the ICMV 2023"},{"key":"ref_11","unstructured":"Qin, X., Wang, Z., Bai, Y., Xie, X., and Jia, H. (2020, January 7\u201312). FFA-Net: Feature fusion attention network for single image dehazing. Proceedings of the AAAI Conference on Artificial Intelligence 2020, New York, NY, USA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2535","DOI":"10.1109\/TGRS.2020.3004556","article-title":"RSDehazeNet: Dehazing network with channel refinement for multispectral remote sensing images","volume":"59","author":"Guo","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","first-page":"1","article-title":"An effective network integrating residual learning and channel attention mechanism for thin cloud removal","volume":"19","author":"Wen","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1927","DOI":"10.1109\/TIP.2023.3256763","article-title":"Vision transformers for single image dehazing","volume":"32","author":"Song","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kulkarni, A., and Murala, S. (2023, January 3\u20137). Aerial image dehazing with attentive deformable transformers. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV56688.2023.00624"},{"key":"ref_16","first-page":"1","article-title":"Learning an effective transformer for remote sensing satellite image dehazing","volume":"20","author":"Song","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","unstructured":"Zhou, H., Wu, X., Chen, H., Chen, X., and He, X. (2024). Rsdehamba: Lightweight vision mamba for remote sensing satellite image dehazing. arXiv."},{"key":"ref_18","unstructured":"Fu, H., Sun, G., Li, Y., Ren, J., Zhang, A., Jing, C., and Ghamisi, P. (2024). HDMba: Hyperspectral remote sensing imagery dehazing with state space model. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9095","DOI":"10.1007\/s13042-025-02744-4","article-title":"Semi-supervised dehazing method based on image enhancement and multi-negative contrastive auxiliary learning","volume":"16","author":"Hou","year":"2025","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ancuti, C.O., Ancuti, C., Timofte, R., and De Vleeschouwer, C. (2018, January 18\u201322). O-haze: A dehazing benchmark with real hazy and haze-free outdoor images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00119"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"11070","DOI":"10.1109\/TCSVT.2024.3414677","article-title":"Bridging the Gap Between Haze Scenarios: A Unified Image Dehazing Model","volume":"34","author":"Feng","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4705014","DOI":"10.1109\/TGRS.2025.3584234","article-title":"Real-World Remote Sensing Image Dehazing: Benchmark and Baseline","volume":"63","author":"Zhu","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5526714","DOI":"10.1109\/TGRS.2023.3321294","article-title":"AACNet: Asymmetric attention convolution network for hyperspectral image dehazing","volume":"61","author":"Xu","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1016\/j.isprsjprs.2024.09.034","article-title":"HyperDehazing: A hyperspectral image dehazing benchmark dataset and a deep learning model for haze removal","volume":"218","author":"Fu","year":"2024","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","first-page":"5512512","article-title":"Fog model-based hyperspectral image defogging","volume":"60","author":"Kang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1109\/JSTARS.2018.2812726","article-title":"Dehazing for multispectral remote sensing images based on a convolutional neural network with the residual architecture","volume":"11","author":"Qin","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0034-4257(93)90013-N","article-title":"The spectral image processing system (SIPS)\u2014interactive visualization and analysis of imaging spectrometer data","volume":"44","author":"Kruse","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., and Wang, O. (2018, January 18\u201322). The unreasonable effectiveness of deep features as a perceptual metric. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3888","DOI":"10.1109\/TIP.2015.2456502","article-title":"Referenceless prediction of perceptual fog density and perceptual image defogging","volume":"24","author":"Choi","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1109\/TNNLS.2017.2649101","article-title":"Learning a no-reference quality assessment model of enhanced images with big data","volume":"29","author":"Gu","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3934","DOI":"10.1109\/TMM.2022.3168438","article-title":"Visibility and distortion measurement for no-reference dehazed image quality assessment via complex contourlet transform","volume":"25","author":"Guan","year":"2022","journal-title":"IEEE Trans. Multimed."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tang, P.W., and Lin, C.H. (2022, January 13\u201316). Hyperspectral Dehazing Using Admm-Adam Theory. Proceedings of the 2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Rome, Italy.","DOI":"10.1109\/WHISPERS56178.2022.9955137"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.1109\/ICCV.1999.790410","article-title":"Object recognition from local scale-invariant features","volume":"Volume 2","author":"Lowe","year":"1999","journal-title":"Proceedings of the Seventh IEEE International Conference on Computer Vision"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Magnusson, M., Sigurdsson, J., Armansson, S.E., Ulfarsson, M.O., Deborah, H., and Sveinsson, J.R. (October, January 26). Creating RGB images from hyperspectral images using a color matching function. Proceedings of the IGARSS 2020\u20142020 IEEE International Geoscience and Remote Sensing Symposium, online.","DOI":"10.1109\/IGARSS39084.2020.9323397"},{"key":"ref_37","first-page":"S283","article-title":"Equalization of Shooting Conditions Based on Spectral Models for the Needs of Precision Agriculture Using UAVs","volume":"67","author":"Pavlova","year":"2022","journal-title":"JCTE"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"451","DOI":"10.18287\/-6179-CO-1235","article-title":"Low-parameter method for delineation of agricultural fields in satellite images based on multi-temporal MSAVI2 data","volume":"47","author":"Pavlova","year":"2023","journal-title":"Comput. Opt."},{"key":"ref_39","unstructured":"Lavreniuk, M., Kussul, N., Shelestov, A., Yailymov, B., Salii, Y., Kuzin, V., and Szantoi, Z. (2025). Delineate Anything: Resolution-Agnostic Field Boundary Delineation on Satellite Imagery. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1109\/JSTARS.2019.2918242","article-title":"Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification","volume":"12","author":"Helber","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_41","unstructured":"Chong, E. (2025, November 19). EuroSAT Land Use and Land Cover Classification Using Deep Learning. Available online: https:\/\/github.com\/e-chong\/Remote-Sensing."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Naushad, R., Kaur, T., and Ghaderpour, E. (2021). Deep transfer learning for land use and land cover classification: A comparative study. Sensors, 21.","DOI":"10.3390\/s21238083"},{"key":"ref_43","first-page":"2567","article-title":"Image quality assessment: Unifying structure and texture similarity","volume":"44","author":"Ding","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Sidorchuk, D.S., Nurmukhametov, A.L., Maximov, P.V., Bozhkova, V.P., Sarycheva, A.P., Pavlova, M.A., Kazakova, A.A., Gracheva, M.A., and Nikolaev, D.P. (2025). Leveraging Achromatic Component for Trichromat-Friendly Daltonization. J. Imaging, 11.","DOI":"10.3390\/jimaging11070225"},{"key":"ref_45","unstructured":"Li, S., Ma, H., and Hu, X. (2021). Neural image beauty predictor based on bradley-terry model. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1134\/S1064226924700104","article-title":"Harmonization of hyperspectral and multispectral data for calculation of vegetation index","volume":"69","author":"Nurmukhametov","year":"2024","journal-title":"J. Commun. Technol. Electron."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"11741","DOI":"10.1109\/JSTARS.2024.3411032","article-title":"CERMF-Net: A SAR-Optical feature fusion for cloud elimination from Sentinel-2 imagery using residual multiscale dilated network","volume":"17","author":"Anandakrishnan","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_48","first-page":"101601","article-title":"Enhancing oil spill detection with controlled random sampling: A multimodal fusion approach using SAR and HSI imagery","volume":"38","author":"Liu","year":"2025","journal-title":"Remote Sens. Appl. Soc. Environ."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/12\/422\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T12:27:13Z","timestamp":1764160033000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/12\/422"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,26]]},"references-count":48,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["jimaging11120422"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11120422","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,26]]}}}