{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T11:08:59Z","timestamp":1776510539910,"version":"3.51.2"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T00:00:00Z","timestamp":1748563200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T00:00:00Z","timestamp":1748563200000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s00521-025-11315-1","type":"journal-article","created":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T14:15:19Z","timestamp":1748614519000},"page":"17273-17291","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["AMBER: advanced SegFormer for multi-band image segmentation\u2014an application to hyperspectral imaging"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5943-6867","authenticated-orcid":false,"given":"Andrea","family":"Dosi","sequence":"first","affiliation":[]},{"given":"Massimo","family":"Brescia","sequence":"additional","affiliation":[]},{"given":"Stefano","family":"Cavuoti","sequence":"additional","affiliation":[]},{"given":"Mariarca","family":"D\u2019Aniello","sequence":"additional","affiliation":[]},{"given":"Michele","family":"Delli Veneri","sequence":"additional","affiliation":[]},{"given":"Carlo","family":"Donadio","sequence":"additional","affiliation":[]},{"given":"Adriano","family":"Ettari","sequence":"additional","affiliation":[]},{"given":"Giuseppe","family":"Longo","sequence":"additional","affiliation":[]},{"given":"Alvi","family":"Rownok","sequence":"additional","affiliation":[]},{"given":"Luca","family":"Sannino","sequence":"additional","affiliation":[]},{"given":"Maria","family":"Zampella","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,30]]},"reference":[{"key":"11315_CR1","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1109\/JSTARS.2021.3133021","volume":"15","author":"M Ahmad","year":"2022","unstructured":"Ahmad M, Shabbir S, Roy SK, Hong D, Wu X, Yao J, Khan AM, Mazzara M, Distefano S, Chanussot J (2022) Hyperspectral image classification-traditional to deep models: a survey for future prospects. IEEE J Select Topics Appl Earth Observ Remote Sensing 15:968\u2013999. https:\/\/doi.org\/10.1109\/JSTARS.2021.3133021","journal-title":"IEEE J Select Topics Appl Earth Observ Remote Sensing"},{"issue":"2","key":"11315_CR2","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1109\/MGRS.2019.2912563","volume":"7","author":"N Audebert","year":"2019","unstructured":"Audebert N, Le Saux B, Lefevre S (2019) Deep learning for classification of hyperspectral data: a comparative review. IEEE Geosci Remote Sensing Magazine 7(2):159\u2013173. https:\/\/doi.org\/10.1109\/MGRS.2019.2912563","journal-title":"IEEE Geosci Remote Sensing Magazine"},{"key":"11315_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2024.3361553","volume":"62","author":"J Fang","year":"2024","unstructured":"Fang J, Yang J, Khader A, Xiao L (2024) Mimo-sst: multi-input multi-output spatial-spectral transformer for hyperspectral and multispectral image fusion. IEEE Trans Geosci Remote Sensing 62:1\u201320. https:\/\/doi.org\/10.1109\/TGRS.2024.3361553","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"11315_CR4","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2021) An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale"},{"key":"11315_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2021.3130716","volume":"60","author":"D Hong","year":"2022","unstructured":"Hong D, Han Z, Yao J, Gao L, Zhang B, Plaza A, Chanussot J (2022) 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":"11315_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2023.3297858","volume":"61","author":"X Zhang","year":"2023","unstructured":"Zhang X, Su Y, Gao L, Bruzzone L, Gu X, Tian Q (2023) A lightweight transformer network for hyperspectral image classification. IEEE Trans Geosci Remote Sensing 61:1\u201317. https:\/\/doi.org\/10.1109\/TGRS.2023.3297858","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"11315_CR7","doi-asserted-by":"publisher","first-page":"17681","DOI":"10.1109\/jstars.2024.3461851","volume":"17","author":"M Ahmad","year":"2024","unstructured":"Ahmad M, Butt MHF, Mazzara M, Distefano S, Khan AM, Altuwaijri HA (2024) Pyramid hierarchical spatial-spectral transformer for hyperspectral image classification. IEEE J Select Topics Appl Earth Observ Remote Sensing 17:17681\u201317689. https:\/\/doi.org\/10.1109\/jstars.2024.3461851","journal-title":"IEEE J Select Topics Appl Earth Observ Remote Sensing"},{"key":"11315_CR8","doi-asserted-by":"publisher","first-page":"103773","DOI":"10.1016\/j.jag.2024.103773","volume":"129","author":"MHF Butt","year":"2024","unstructured":"Butt MHF, Li JP, Ahmad M, Butt MAF (2024) Graph-infused hybrid vision transformer: advancing geoai for enhanced land cover classification. Int J Appl Earth Observ Geoinform 129:103773. https:\/\/doi.org\/10.1016\/j.jag.2024.103773","journal-title":"Int J Appl Earth Observ Geoinform"},{"key":"11315_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tgrs.2022.3196057","volume":"60","author":"P Ghosh","year":"2022","unstructured":"Ghosh P, Roy SK, Koirala B, Rasti B, Scheunders P (2022) Hyperspectral unmixing using transformer network. IEEE Trans Geosci Remote Sens 60:1\u201316. https:\/\/doi.org\/10.1109\/tgrs.2022.3196057","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"11315_CR10","doi-asserted-by":"publisher","first-page":"102408","DOI":"10.1016\/j.inffus.2024.102408","volume":"108","author":"C Li","year":"2024","unstructured":"Li C, Zhang B, Hong D, Zhou J, Vivone G, Li S, Chanussot J (2024) Casformer: cascaded transformers for fusion-aware computational hyperspectral imaging. Inform Fusion 108:102408. https:\/\/doi.org\/10.1016\/j.inffus.2024.102408","journal-title":"Inform Fusion"},{"key":"11315_CR11","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":"11315_CR12","unstructured":"Xie E, Wang W, Yu Z, Anandkumar A., Alvarez J.M, Luo P (2021). SegFormer: simple and efficient design for semantic segmentation with transformers"},{"key":"11315_CR13","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-05422-5","author":"Q Shenming","year":"2022","unstructured":"Shenming Q, Xiang L, Zhihua G (2022) A new hyperspectral image classification method based on spatial-spectral features. Sci Rep. https:\/\/doi.org\/10.1038\/s41598-022-05422-5","journal-title":"Sci Rep"},{"key":"11315_CR14","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1007\/978-3-031-25755-1_22","volume-title":"The Use of Artificial Intelligence for Space Applications","author":"A Dosi","year":"2023","unstructured":"Dosi A, Pesce M, Di Nardo A, Pafundi V, Delli Veneri M, Chirico R, Ammirati L, Mondillo N, Longo G (2023) Prisma hyperspectral image segmentation with u-net convolutional neural network using singular value decomposition for mapping mining areas: Preliminary results. In: Ieracitano C, Mammone N, Di Clemente M, Mahmud M, Furfaro R, Morabito FC (eds) The Use of Artificial Intelligence for Space Applications. Springer, Cham, pp 327\u2013340"},{"issue":"9","key":"11315_CR15","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1080\/2150704X.2017.1331053","volume":"8","author":"B Liu","year":"2017","unstructured":"Liu B, Yu X, Zhang P, Tan X, Yu A, Xue Z (2017) A semi-supervised convolutional neural network for hyperspectral image classification. Remote Sensing Lett 8(9):839\u2013848. https:\/\/doi.org\/10.1080\/2150704X.2017.1331053","journal-title":"Remote Sensing Lett"},{"issue":"2","key":"11315_CR16","doi-asserted-by":"publisher","first-page":"847","DOI":"10.1109\/TGRS.2017.2755542","volume":"56","author":"Z Zhong","year":"2018","unstructured":"Zhong Z, Li J, Luo Z, Chapman M (2018) Spectral-spatial residual network for hyperspectral image classification: a 3-d deep learning framework. IEEE Trans Geosci Remote Sens 56(2):847\u2013858. https:\/\/doi.org\/10.1109\/TGRS.2017.2755542","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"2","key":"11315_CR17","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1109\/TGRS.2018.2860125","volume":"57","author":"ME Paoletti","year":"2019","unstructured":"Paoletti ME, Haut JM, Fernandez-Beltran R, Plaza J, Plaza AJ, Pla F (2019) Deep pyramidal residual networks for spectral-spatial hyperspectral image classification. IEEE Trans Geosci Remote Sens 57(2):740\u2013754. https:\/\/doi.org\/10.1109\/TGRS.2018.2860125","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"11315_CR18","doi-asserted-by":"publisher","first-page":"121032","DOI":"10.1016\/j.eswa.2023.121032D","volume":"234","author":"F Zhao","year":"2023","unstructured":"Zhao F, Zhang J, Meng Z, Liu H, Chang Z, Fan J (2023) Multiple vision architectures-based hybrid network for hyperspectral image classification. Expert Syst Appl 234:121032. https:\/\/doi.org\/10.1016\/j.eswa.2023.121032D","journal-title":"Expert Syst Appl"},{"key":"11315_CR19","doi-asserted-by":"crossref","unstructured":"Chhapariya K, Buddhiraju K.M, Kumar A (2022) Spectral-spatial classification of hyperspectral images with multi-level cnn. In: 2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp. 1\u20135","DOI":"10.1109\/WHISPERS56178.2022.9955063"},{"key":"11315_CR20","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A.N, Kaiser L, Polosukhin I (2023). Attention Is All You Need. arXiv:abs\/1706.03762"},{"key":"11315_CR21","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":"11315_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3207933","volume":"60","author":"S Mei","year":"2022","unstructured":"Mei S, Song C, Ma M, Xu F (2022) Hyperspectral image classification using group-aware hierarchical transformer. IEEE Trans Geosci Remote Sens 60:1\u201314. https:\/\/doi.org\/10.1109\/TGRS.2022.3207933","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"11315_CR23","doi-asserted-by":"publisher","DOI":"10.3390\/rs13030498","author":"X He","year":"2021","unstructured":"He X, Chen Y, Lin Z (2021) Spatial-spectral transformer for hyperspectral image classification. Remote Sensing. https:\/\/doi.org\/10.3390\/rs13030498","journal-title":"Remote Sensing"},{"key":"11315_CR24","unstructured":"Ehu.eus: Hyperspectral Remote Sensing Scenes (2017).[online]. https:\/\/www.ehu.eus\/ccwintco\/index.php?title=Hyperspectral_Remote_Sensing_Scenes"},{"key":"11315_CR25","doi-asserted-by":"publisher","DOI":"10.1594\/PANGAEA.910894","author":"V Maus","year":"2020","unstructured":"Maus V, Giljum S, Gutschlhofer J, da Silva DM, Probst M, Gass SLB, Luckeneder S, Lieber M, McCallum I (2020) Global-scale mining polygons (Version 1). PANGAEA. https:\/\/doi.org\/10.1594\/PANGAEA.910894","journal-title":"PANGAEA"},{"key":"11315_CR26","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.isprsjprs.2017.11.021","volume":"145","author":"ME Paoletti","year":"2018","unstructured":"Paoletti ME, Haut JM, Plaza J, Plaza A (2018) A new deep convolutional neural network for fast hyperspectral image classification. ISPRS J Photogram Remote Sensing 145:120\u2013147. https:\/\/doi.org\/10.1016\/j.isprsjprs.2017.11.021","journal-title":"ISPRS J Photogram Remote Sensing"},{"key":"11315_CR27","doi-asserted-by":"publisher","DOI":"10.3390\/rs15153793","author":"H Feng","year":"2023","unstructured":"Feng H, Wang Y, Li Z, Zhang N, Zhang Y, Gao Y (2023) Information leakage in deep learning-based hyperspectral image classification: a survey. Remote Sensing. https:\/\/doi.org\/10.3390\/rs15153793","journal-title":"Remote Sensing"},{"key":"11315_CR28","doi-asserted-by":"publisher","first-page":"20819","DOI":"10.1007\/s11042-022-13959-w","volume":"82","author":"R Grewal","year":"2022","unstructured":"Grewal R, Kasana SS, Kasana G (2022) Hyperspectral image segmentation: a comprehensive survey. Multimed Tools Appl 82:20819\u201320872","journal-title":"Multimed Tools Appl"},{"issue":"1","key":"11315_CR29","doi-asserted-by":"publisher","first-page":"67","DOI":"10.3390\/rs9010067","volume":"9","author":"Y Li","year":"2017","unstructured":"Li Y, Zhang H, Shen Q (2017) Spectral\u2013spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sensing 9(1):67. https:\/\/doi.org\/10.3390\/rs9010067","journal-title":"Remote Sensing"},{"key":"11315_CR30","doi-asserted-by":"publisher","DOI":"10.3390\/rs8020099","author":"H Liang","year":"2016","unstructured":"Liang H, Li Q (2016) Hyperspectral imagery classification using sparse representations of convolutional neural network features. Remote Sensing. https:\/\/doi.org\/10.3390\/rs8020099","journal-title":"Remote Sensing"},{"key":"11315_CR31","doi-asserted-by":"crossref","unstructured":"Luo Y, Zou J, Yao C, Li T, Bai G (2018). HSI-CNN: a novel convolution neural network for hyperspectral image","DOI":"10.1109\/ICALIP.2018.8455251"},{"key":"11315_CR32","doi-asserted-by":"crossref","unstructured":"He M, Li B, Chen, H (2017). Multi-scale 3d deep convolutional neural network for hyperspectral image classification. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 3904\u20133908","DOI":"10.1109\/ICIP.2017.8297014"},{"key":"11315_CR33","doi-asserted-by":"crossref","unstructured":"Lee H, Kwon H (2016). Contextual deep cnn based hyperspectral classification. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 3322\u20133325","DOI":"10.1109\/IGARSS.2016.7729859"},{"issue":"10","key":"11315_CR34","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":"8","key":"11315_CR35","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1109\/lgrs.2019.2895697","volume":"16","author":"J Nalepa","year":"2019","unstructured":"Nalepa J, Myller M, Kawulok M (2019) Validating hyperspectral image segmentation. IEEE Geosci Remote Sens Lett 16(8):1264\u20131268. https:\/\/doi.org\/10.1109\/lgrs.2019.2895697","journal-title":"IEEE Geosci Remote Sens Lett"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11315-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11315-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11315-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T16:31:43Z","timestamp":1757176303000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11315-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,30]]},"references-count":35,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["11315"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11315-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,30]]},"assertion":[{"value":"6 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}