{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T11:33:31Z","timestamp":1772192011881,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T00:00:00Z","timestamp":1772150400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T00:00:00Z","timestamp":1772150400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-026-21460-x","type":"journal-article","created":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T10:39:28Z","timestamp":1772188768000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-branch multi-attention framework for hyperspectral image classification (MB-MA-HIC)"],"prefix":"10.1007","volume":"85","author":[{"given":"Mohammad","family":"Ahangar Kiasari","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leila","family":"Talebi Jouneghani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4628-5238","authenticated-orcid":false,"given":"Amirhossein","family":"Nikoofard","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ik","family":"Hyun Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,27]]},"reference":[{"key":"21460_CR1","doi-asserted-by":"crossref","unstructured":"Teng MY, Mehrubeoglu R, King SA, Cammarata K, Simons J (2013) Investigation of epifauna coverage on seagrass blades using spatial and spectral analysis of hyperspectral images. In: 2013 5th Workshop on hyperspectral image and signal processing: Evolution in remote sensing (WHISPERS) (IEEE), pp 1\u20134","DOI":"10.1109\/WHISPERS.2013.8080658"},{"key":"21460_CR2","doi-asserted-by":"publisher","unstructured":"Zhao M, Yu C, Song M, Chang CI (2018) A semantic feature extraction method for hyperspectral image classification based on hashing learning. In: 2018 9th Workshop on hyperspectral image and signal processing: Evolution in remote sensing (WHISPERS) (IEEE), pp 1\u20135. https:\/\/doi.org\/10.1109\/WHISPERS.2018.8747106","DOI":"10.1109\/WHISPERS.2018.8747106"},{"key":"21460_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2021.3049267","volume":"19","author":"C Yu","year":"2021","unstructured":"Yu C, Zhou S, Song M, Chang CI (2021) Semisupervised hyperspectral band selection based on dual-constrained low-rank representation. IEEE Geosci Remote Sens Lett 19:1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2021.3049267","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"3","key":"21460_CR4","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1109\/TIM.2009.2005557","volume":"58","author":"H Erives","year":"2009","unstructured":"Erives H, Targhetta NB (2009) Implementation of a 3-d hyperspectral instrument for skin imaging applications. IEEE Trans Instrum Meas 58(3):631\u2013638. https:\/\/doi.org\/10.1109\/TIM.2009.2005557","journal-title":"IEEE Trans Instrum Meas"},{"key":"21460_CR5","doi-asserted-by":"publisher","unstructured":"Moughal T (2013) Hyperspectral image classification using support vector machine, in journal of physics: conference series, vol 439 (IOP Publishing), p 012042. https:\/\/doi.org\/10.1088\/1742-6596\/439\/1\/012042","DOI":"10.1088\/1742-6596\/439\/1\/012042"},{"key":"21460_CR6","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2024.3414392","author":"A Qin","year":"2024","unstructured":"Qin A, Yuan C, Li Q, Luo X, Yang F, Song T, Gao C (2024) Few-shot learning with prototype rectification for cross-domain hyperspectral image classification. IEEE Trans Geosci Remote Sens. https:\/\/doi.org\/10.1109\/TGRS.2024.3414392","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"21460_CR7","doi-asserted-by":"crossref","unstructured":"Reyes-Angulo AA, Paheding S (2024) Forward-Forward Algorithm for Hyperspectral Image Classification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3153\u20133161","DOI":"10.1109\/CVPRW63382.2024.00321"},{"key":"21460_CR8","doi-asserted-by":"publisher","first-page":"27061","DOI":"10.1007\/s11042-018-5904-x","volume":"77","author":"S Singh","year":"2018","unstructured":"Singh S, Kasana SS (2018) Efficient classification of the hyperspectral images using deep learning. Multimed Tools Appl 77:27061\u201327074","journal-title":"Multimed Tools Appl"},{"key":"21460_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2015\/258619","volume":"2015","author":"W Hu","year":"2015","unstructured":"Hu W, Huang Y, Wei L, Zhang F, Li H (2015) Deep convolutional neural networks for hyperspectral image classification. J Sensors 2015:1\u201312","journal-title":"J Sensors"},{"issue":"8","key":"21460_CR10","doi-asserted-by":"publisher","first-page":"4544","DOI":"10.1109\/TGRS.2016.2543748","volume":"54","author":"W Zhao","year":"2016","unstructured":"Zhao W, Du S (2016) Spectral-spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach. IEEE Trans Geosci Remote Sens 54(8):4544\u20134554. https:\/\/doi.org\/10.1109\/TGRS.2016.2543748","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"10","key":"21460_CR11","doi-asserted-by":"publisher","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","volume":"54","author":"Y Chen","year":"2016","unstructured":"Chen Y, Jiang H, Li C, Jia X, Ghamisi P (2016) Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 54(10):6232\u20136251. https:\/\/doi.org\/10.1109\/TGRS.2016.2584107","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"2","key":"21460_CR12","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1109\/LGRS.2019.2918719","volume":"17","author":"SK Roy","year":"2019","unstructured":"Roy SK, Krishna G, Dubey SR, Chaudhuri BB (2019) Hybridsn: Exploring 3-d-2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geosci Remote Sens Lett 17(2):277\u2013281. https:\/\/doi.org\/10.1109\/LGRS.2019.2918719","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"21460_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/4608647","volume":"2020","author":"C Zhao","year":"2020","unstructured":"Zhao C, Zhao H, Wang G, Chen H (2020) Hybrid depth-separable residual networks for hyperspectral image classification. Complexity 2020:1\u201317. https:\/\/doi.org\/10.1155\/2020\/4608647","journal-title":"Complexity"},{"issue":"23","key":"21460_CR14","doi-asserted-by":"publisher","first-page":"32723","DOI":"10.1007\/s11042-022-12679-5","volume":"81","author":"PP Vaish","year":"2022","unstructured":"Vaish PP, Rani K, Kumar S (2022) Cyclic learning rate based hybridsn model for hyperspectral image classification. Multimed Tools Appl 81(23):32723\u201332738. https:\/\/doi.org\/10.1007\/s11042-022-12679-5","journal-title":"Multimed Tools Appl"},{"issue":"3","key":"21460_CR15","doi-asserted-by":"publisher","first-page":"582","DOI":"10.3390\/rs12030582","volume":"12","author":"R Li","year":"2020","unstructured":"Li R, Zheng S, Duan C, Yang Y, Wang X (2020) Classification of hyperspectral image based on double-branch dual-attention mechanism network. Remote Sensing 12(3):582. https:\/\/doi.org\/10.3390\/rs12030582","journal-title":"Remote Sensing"},{"issue":"23","key":"21460_CR16","doi-asserted-by":"publisher","first-page":"6158","DOI":"10.3390\/rs14236158","volume":"14","author":"W Huang","year":"2022","unstructured":"Huang W, Zhao Z, Sun L, Ju M (2022) Dual-branch attention-assisted cnn for hyperspectral image classification. Remote Sensing 14(23):6158. https:\/\/doi.org\/10.3390\/rs14236158","journal-title":"Remote Sensing"},{"key":"21460_CR17","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3271713","author":"B Tu","year":"2023","unstructured":"Tu B, Ren Q, Li Q, He W, He W (2023) Hyperspectral image classification using a superpixel-pixel-subpixel multilevel network. IEEE Trans Instrum Meas. https:\/\/doi.org\/10.1109\/TIM.2023.3271713","journal-title":"IEEE Trans Instrum Meas"},{"key":"21460_CR18","doi-asserted-by":"publisher","first-page":"5455","DOI":"10.1109\/JSTARS.2022.3188732","volume":"15","author":"S Ghaderizadeh","year":"2022","unstructured":"Ghaderizadeh S, Abbasi-Moghadam D, Sharifi A, Tariq A, Qin S (2022) Multiscale dual-branch residual spectral-spatial network with attention for hyperspectral image classification. IEEE J Selec Topics Appl Earth Observ Remote Sens 15:5455\u20135467. https:\/\/doi.org\/10.1109\/JSTARS.2022.3188732","journal-title":"IEEE J Selec Topics Appl Earth Observ Remote Sens"},{"key":"21460_CR19","doi-asserted-by":"publisher","unstructured":"Yu W, Huang H, Zhang M, Shen Y (2023) Stacked Dual-stream LSTM based Feature Extraction Network for Hyperspectral Image Classification. In: 2023 42nd Chinese control conference (CCC) (IEEE), pp 7417\u20137420. https:\/\/doi.org\/10.23919\/CCC58697.2023.10240536","DOI":"10.23919\/CCC58697.2023.10240536"},{"key":"21460_CR20","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3344782","author":"F Zhou","year":"2023","unstructured":"Zhou F, Xu C, Yang G, Hang R, Liu Q (2023) Masked spectral-spatial feature prediction for hyperspectral image classification. IEEE Trans Geosci Remote Sens. https:\/\/doi.org\/10.1109\/TGRS.2023.3344782","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"21460_CR21","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998\u20136008"},{"key":"21460_CR22","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"key":"21460_CR23","doi-asserted-by":"crossref","unstructured":"Zhang Z, Jiang Y, Jiang J, Wang X, Luo P, Gu J (2021) STAR: A structure-aware lightweight transformer for real-time image enhancement. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 4106\u20134115","DOI":"10.1109\/ICCV48922.2021.00407"},{"key":"21460_CR24","doi-asserted-by":"crossref","unstructured":"Chen Z, Zhu Y, Zhao C, Hu G, Zeng W, Wang J, Tang M (2021) Dpt: Deformable patch-based transformer for visual recognition. In: Proceedings of the 29th ACM international conference on multimedia, pp 2899\u20132907","DOI":"10.1145\/3474085.3475467"},{"key":"21460_CR25","doi-asserted-by":"publisher","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: European conference on computer vision (Springer), pp 213\u2013229. https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"21460_CR26","doi-asserted-by":"publisher","unstructured":"Strudel R, Garcia R, Laptev I, Schmid C (2021) Segmenter: Transformer for semantic segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 7262\u20137272. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00717","DOI":"10.1109\/ICCV48922.2021.00717"},{"key":"21460_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3144158","volume":"60","author":"L Sun","year":"2022","unstructured":"Sun L, Zhao G, Zheng Y, Wu Z (2022) Spectral-spatial feature tokenization transformer for hyperspectral image classification. IEEE Trans Geosci Remote Sens 60:1\u201314. https:\/\/doi.org\/10.1109\/TGRS.2022.3144158","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"21460_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3171551","volume":"60","author":"X Yang","year":"2022","unstructured":"Yang X, Cao W, Lu Y, Zhou Y (2022) Hyperspectral image transformer classification networks. IEEE Trans Geosci Remote Sens 60:1\u201315. https:\/\/doi.org\/10.1109\/TGRS.2022.3171551","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"21460_CR29","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2024.3368141","author":"R Xu","year":"2024","unstructured":"Xu R, Dong XM, Li W, Peng J, Sun W, Xu Y (2024) Dbctnet: Double branch convolution-transformer network for hyperspectral image classification. IEEE Trans Geosci Remote Sens. https:\/\/doi.org\/10.1109\/TGRS.2024.3368141","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"21460_CR30","doi-asserted-by":"crossref","unstructured":"Kherif F, Latypova A (2020) In: Machine learning (Elsevier), pp 209\u2013225","DOI":"10.1016\/B978-0-12-815739-8.00012-2"},{"key":"21460_CR31","doi-asserted-by":"crossref","unstructured":"Vidal R, Ma Y, Sastry SS, Vidal R, Ma Y, Sastry SS (2016) Principal component analysis. General Princip Compon Anal 25\u201362","DOI":"10.1007\/978-0-387-87811-9_2"},{"issue":"3","key":"21460_CR32","doi-asserted-by":"publisher","first-page":"498","DOI":"10.3390\/rs13030498","volume":"13","author":"X He","year":"2021","unstructured":"He X, Chen Y, Lin Z (2021) Spatial-spectral transformer for hyperspectral image classification. Remote Sens 13(3):498. https:\/\/doi.org\/10.3390\/rs13030498","journal-title":"Remote Sens"},{"key":"21460_CR33","doi-asserted-by":"publisher","first-page":"4307","DOI":"10.1109\/JSTARS.2022.3174135","volume":"15","author":"Z Xue","year":"2022","unstructured":"Xue Z, Xu Q, Zhang M (2022) Local transformer with spatial partition restore for hyperspectral image classification. IEEE J Selec Topics Appl Earth Observ Remote Sens 15:4307\u20134325. https:\/\/doi.org\/10.1109\/JSTARS.2022.3174135","journal-title":"IEEE J Selec Topics Appl Earth Observ Remote Sens"},{"key":"21460_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3186400","volume":"60","author":"H Yu","year":"2022","unstructured":"Yu H, Xu Z, Zheng K, Hong D, Yang H, Song M (2022) Mstnet: A multilevel spectral-spatial transformer network for hyperspectral image classification. IEEE Trans Geosci Remote Sens 60:1\u201313. https:\/\/doi.org\/10.1109\/TGRS.2022.3186400","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"21460_CR35","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3235711","author":"J Bai","year":"2023","unstructured":"Bai J, Shi W, Xiao Z, Ali TAA, Ye F, Jiao L (2023) Achieving better category separability for hyperspectral image classification: A spatial-spectral approach. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2023.3235711","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"21460_CR36","doi-asserted-by":"publisher","first-page":"119508","DOI":"10.1016\/j.eswa.2023.119508","volume":"217","author":"Z Zhang","year":"2023","unstructured":"Zhang Z, Ding Y, Zhao X, Siye L, Yang N, Cai Y, Zhan Y (2023) Multireceptive field: An adaptive path aggregation graph neural framework for hyperspectral image classification. Expert Syst Appl 217:119508. https:\/\/doi.org\/10.1016\/j.eswa.2023.119508","journal-title":"Expert Syst Appl"},{"issue":"7","key":"21460_CR37","doi-asserted-by":"publisher","first-page":"1571","DOI":"10.3390\/rs14071571","volume":"14","author":"YL Chang","year":"2022","unstructured":"Chang YL, Tan TH, Lee WH, Chang L, Chen YN, Fan KC, Alkhaleefah M (2022) Consolidated convolutional neural network for hyperspectral image classification. Remote Sensing 14(7):1571","journal-title":"Remote Sensing"},{"issue":"1","key":"21460_CR38","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1038\/s41598-023-27472-z","volume":"13","author":"L Dang","year":"2023","unstructured":"Dang L, Weng L, Hou Y, Zuo X, Liu Y (2023) Double-branch feature fusion transformer for hyperspectral image classification. Sci Rep 13(1):272. https:\/\/doi.org\/10.1038\/s41598-023-27472-z","journal-title":"Sci Rep"},{"key":"21460_CR39","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1109\/JSTARS.2020.2968179","volume":"13","author":"L Zou","year":"2020","unstructured":"Zou L, Zhu X, Wu C, Liu Y, Qu L (2020) Spectral-spatial exploration for hyperspectral image classification via the fusion of fully convolutional networks. IEEE J Selec Topics Appl Earth Observ Remote Sens 13:659\u2013674. https:\/\/doi.org\/10.1109\/JSTARS.2020.2968179","journal-title":"IEEE J Selec Topics Appl Earth Observ Remote Sens"},{"issue":"7","key":"21460_CR40","doi-asserted-by":"publisher","first-page":"1646","DOI":"10.1109\/TIM.2017.2664480","volume":"66","author":"L Fang","year":"2017","unstructured":"Fang L, Wang C, Li S, Benediktsson JA (2017) Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Trans Instrum Meas 66(7):1646\u20131657. https:\/\/doi.org\/10.1109\/TIM.2017.2664480","journal-title":"IEEE Trans Instrum Meas"},{"key":"21460_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2021.3130716","volume":"60","author":"D Hong","year":"2021","unstructured":"Hong D, Han Z, Yao J, Gao L, Zhang B, Plaza A, Chanussot J (2021) Spectralformer: Rethinking hyperspectral image classification with transformers. IEEE Trans Geosci Remote Sens 60:1\u201315. https:\/\/doi.org\/10.1109\/TGRS.2021.3130716","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"21460_CR42","unstructured":"Kingma DP, Adam JB (2014) A method for stochastic optimization. arXiv preprint arXiv:1412.6980"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21460-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-026-21460-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21460-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T10:39:31Z","timestamp":1772188771000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-026-21460-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,27]]},"references-count":42,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["21460"],"URL":"https:\/\/doi.org\/10.1007\/s11042-026-21460-x","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,27]]},"assertion":[{"value":"6 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"This research is not subject to ethical approval.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"241"}}