{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T18:55:33Z","timestamp":1769280933236,"version":"3.49.0"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2023,2,4]],"date-time":"2023-02-04T00:00:00Z","timestamp":1675468800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,2,4]],"date-time":"2023-02-04T00:00:00Z","timestamp":1675468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004541","name":"Ministry of Education, Government of India","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004541","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s00521-023-08306-5","type":"journal-article","created":{"date-parts":[[2023,2,4]],"date-time":"2023-02-04T18:03:01Z","timestamp":1675533781000},"page":"11417-11430","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A lightweight relation network for few-shots classification of hyperspectral images"],"prefix":"10.1007","volume":"35","author":[{"given":"Anshul","family":"Mishra","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6438-8222","authenticated-orcid":false,"given":"Upendra Pratap","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Krishna Pratap","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,4]]},"reference":[{"key":"8306_CR1","doi-asserted-by":"crossref","unstructured":"Alajaji D, Alhichri HS, Ammour N, Alajlan N (2020) Few-shot learning for remote sensing scene classification. In: 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), pp. 81\u201384. IEEE","DOI":"10.1109\/M2GARSS47143.2020.9105154"},{"key":"8306_CR2","doi-asserted-by":"crossref","unstructured":"Alloghani M, Al-Jumeily D, Mustafina J, Hussain A, Aljaaf AJ (2020) A systematic review on supervised and unsupervised machine learning algorithms for data science. Superv Unsuperv Learn Data Sci pp. 3\u201321","DOI":"10.1007\/978-3-030-22475-2_1"},{"issue":"3","key":"8306_CR3","doi-asserted-by":"publisher","first-page":"292","DOI":"10.3390\/electronics8030292","volume":"8","author":"MZ Alom","year":"2019","unstructured":"Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, Hasan M, Van Essen BC, Awwal AA, Asari VK (2019) A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3):292","journal-title":"Electronics"},{"key":"8306_CR4","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.ecoinf.2014.07.004","volume":"24","author":"M Awad","year":"2014","unstructured":"Awad M (2014) Sea water chlorophyll-a estimation using hyperspectral images and supervised artificial neural network. Ecol Inf 24:60\u201368","journal-title":"Ecol Inf"},{"key":"8306_CR5","first-page":"1","volume":"60","author":"J Bai","year":"2022","unstructured":"Bai J, Huang S, Xiao Z, Li X, Zhu Y, Regan AC, Jiao L (2022) Few-shot hyperspectral image classification based on adaptive subspaces and feature transformation. IEEE Trans Geosci Remote Sens 60:1\u201317","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"9","key":"8306_CR6","doi-asserted-by":"publisher","first-page":"3457","DOI":"10.1109\/TGRS.2010.2046494","volume":"48","author":"TC Bau","year":"2010","unstructured":"Bau TC, Sarkar S, Healey G (2010) Hyperspectral region classification using a three-dimensional gabor filterbank. IEEE Trans Geosci Remote Sens 48(9):3457\u20133464. https:\/\/doi.org\/10.1109\/TGRS.2010.2046494","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"10","key":"8306_CR7","first-page":"1331","volume":"49","author":"L Bing","year":"2020","unstructured":"Bing L, Xibing Z, Xiong T, Anzhu Y, Wenyue G (2020) A deep few-shot learning algorithm for hyperspectral image classification. Acta Geodaetica et Cartographica Sinica 49(10):1331","journal-title":"Acta Geodaetica et Cartographica Sinica"},{"key":"8306_CR8","doi-asserted-by":"publisher","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","volume":"7","author":"Y Chen","year":"2014","unstructured":"Chen Y, Lin Z, Zhao X, Wang G, Gu Y (2014) Deep learning-based classification of hyperspectral data. IEEE J Select Topics Appl Earth Observ Remote Sens 7:2094\u20132107. https:\/\/doi.org\/10.1109\/JSTARS.2014.2329330","journal-title":"IEEE J Select Topics Appl Earth Observ Remote Sens"},{"issue":"6","key":"8306_CR9","doi-asserted-by":"publisher","first-page":"2381","DOI":"10.1109\/JSTARS.2015.2388577","volume":"8","author":"Y Chen","year":"2015","unstructured":"Chen Y, Zhao X, Jia X (2015) Spectral-spatial classification of hyperspectral data based on deep belief network. IEEE J Select Topics Appl Earth Observ Remote Sens 8(6):2381\u20132392. https:\/\/doi.org\/10.1109\/JSTARS.2015.2388577","journal-title":"IEEE J Select Topics Appl Earth Observ Remote Sens"},{"key":"8306_CR10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3034752","volume":"60","author":"G Cheng","year":"2021","unstructured":"Cheng G, Cai L, Lang C, Yao X, Chen J, Guo L, Han J (2021) Spnet: Siamese-prototype network for few-shot remote sensing image scene classification. IEEE Trans Geosci Remote Sens 60:1\u201311","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"2","key":"8306_CR11","doi-asserted-by":"publisher","first-page":"1422","DOI":"10.1109\/TGRS.2019.2946318","volume":"58","author":"B Deng","year":"2020","unstructured":"Deng B, Jia S, Shi D (2020) Deep metric learning-based feature embedding for hyperspectral image classification. IEEE Trans Geosci Remote Sens 58(2):1422\u20131435. https:\/\/doi.org\/10.1109\/TGRS.2019.2946318","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"8306_CR12","doi-asserted-by":"crossref","unstructured":"Dhawale AD, Kulkarni SB, Kumbhakarna VM (2020) A survey of distinctive prominence of automatic text summarization techniques using natural language processing. In: International Conference on Mobile Computing and Sustainable Informatics, pp. 543\u2013549. Springer","DOI":"10.1007\/978-3-030-49795-8_52"},{"key":"8306_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2021.100379","volume":"40","author":"S Dong","year":"2021","unstructured":"Dong S, Wang P, Abbas K (2021) A survey on deep learning and its applications. Comput Sci Rev 40:100379","journal-title":"Comput Sci Rev"},{"key":"8306_CR14","doi-asserted-by":"publisher","unstructured":"Falco N, Bruzzone L, Benediktsson JA (2014) An ica based approach to hyperspectral image feature reduction. In: 2014 IEEE Geoscience and Remote Sensing Symposium, pp. 3470\u20133473. https:\/\/doi.org\/10.1109\/IGARSS.2014.6947229","DOI":"10.1109\/IGARSS.2014.6947229"},{"key":"8306_CR15","doi-asserted-by":"publisher","DOI":"10.3390\/rs12060923","author":"K Gao","year":"2020","unstructured":"Gao K, Liu B, Yu X, Qin J, Zhang P, Tan X (2020) Deep relation network for hyperspectral image few-shot classification. Remote Sens. https:\/\/doi.org\/10.3390\/rs12060923","journal-title":"Remote Sens"},{"issue":"8","key":"8306_CR16","doi-asserted-by":"publisher","first-page":"3090","DOI":"10.1080\/01431161.2020.1864060","volume":"42","author":"K Gao","year":"2021","unstructured":"Gao K, Liu B, Yu X, Zhang P, Tan X, Sun Y (2021) Small sample classification of hyperspectral image using model-agnostic meta-learning algorithm and convolutional neural network. Int J Remote Sens 42(8):3090\u20133122","journal-title":"Int J Remote Sens"},{"issue":"10","key":"8306_CR17","doi-asserted-by":"publisher","first-page":"3614","DOI":"10.1109\/TPAMI.2020.2981604","volume":"43","author":"C Geng","year":"2020","unstructured":"Geng C, Huang Sj, Chen S (2020) Recent advances in open set recognition: a survey. IEEE Trans Pattern Anal Mach Intell 43(10):3614\u20133631","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"6","key":"8306_CR18","doi-asserted-by":"publisher","first-page":"3599","DOI":"10.1109\/TGRS.2018.2886022","volume":"57","author":"Z Gong","year":"2019","unstructured":"Gong Z, Zhong P, Yu Y, Hu W, Li S (2019) A cnn with multiscale convolution and diversified metric for hyperspectral image classification. IEEE Trans Geosci Remote Sens 57(6):3599\u20133618. https:\/\/doi.org\/10.1109\/TGRS.2018.2886022","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"4","key":"8306_CR19","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1306","volume":"9","author":"A Handa","year":"2019","unstructured":"Handa A, Sharma A, Shukla SK (2019) Machine learning in cybersecurity: a review. Wiley Interdiscip Rev Data Mining Knowl Discov 9(4):e1306","journal-title":"Wiley Interdiscip Rev Data Mining Knowl Discov"},{"key":"8306_CR20","doi-asserted-by":"publisher","unstructured":"He L, Chen X (2016) A three-dimensional filtering method for spectral-spatial hyperspectral image classification. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2746\u20132748. https:\/\/doi.org\/10.1109\/IGARSS.2016.7729709","DOI":"10.1109\/IGARSS.2016.7729709"},{"issue":"5","key":"8306_CR21","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1549\/5\/052011","volume":"1549","author":"L Hu","year":"2020","unstructured":"Hu L, Luo X, Wei Y (2020) Hyperspectral image classification of convolutional neural network combined with valuable samples. J Phys Conf Ser 1549(5):052011. https:\/\/doi.org\/10.1088\/1742-6596\/1549\/5\/052011","journal-title":"J Phys Conf Ser"},{"key":"8306_CR22","doi-asserted-by":"crossref","unstructured":"Hu Y, Huang Y, Wei G, Zhu K (2022) Heterogeneous few-shot learning with knowledge distillation for hyperspectral image classification. In: 2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE), pp. 601\u2013604. IEEE","DOI":"10.1109\/ICCECE54139.2022.9712772"},{"issue":"1","key":"8306_CR23","doi-asserted-by":"publisher","first-page":"111","DOI":"10.3390\/rs14010111","volume":"14","author":"W Huang","year":"2021","unstructured":"Huang W, Yuan Z, Yang A, Tang C, Luo X (2021) Tae-net: task-adaptive embedding network for few-shot remote sensing scene classification. Remote Sens 14(1):111","journal-title":"Remote Sens"},{"issue":"4","key":"8306_CR24","doi-asserted-by":"publisher","first-page":"2399","DOI":"10.1109\/TGRS.2016.2642951","volume":"55","author":"S Jia","year":"2017","unstructured":"Jia S, Hu J, Zhu J, Jia X, Li Q (2017) Three-dimensional local binary patterns for hyperspectral imagery classification. IEEE Trans Geosci Remote Sens 55(4):2399\u20132413. https:\/\/doi.org\/10.1109\/TGRS.2016.2642951","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"8306_CR25","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.neucom.2021.03.035","volume":"448","author":"S Jia","year":"2021","unstructured":"Jia S, Jiang S, Lin Z, Li N, Xu M, Yu S (2021) A survey: deep learning for hyperspectral image classification with few labeled samples. Neurocomputing 448:179\u2013204","journal-title":"Neurocomputing"},{"key":"8306_CR26","doi-asserted-by":"publisher","first-page":"5585","DOI":"10.1109\/TGRS.2017.2710079","volume":"55","author":"L Jiao","year":"2017","unstructured":"Jiao L, Liang M, Chen H, Yang S, Liu H, Cao X (2017) Deep fully convolutional network-based spatial distribution prediction for hyperspectral image classification. IEEE Trans Geosci Remote Sens 55:5585\u20135599","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"8306_CR27","doi-asserted-by":"crossref","unstructured":"Kadam S, Vaidya V (2018) Review and analysis of zero, one and few shot learning approaches. In: International Conference on Intelligent Systems Design and Applications, pp. 100\u2013112. Springer","DOI":"10.1007\/978-3-030-16657-1_10"},{"issue":"1","key":"8306_CR28","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/s10462-018-09677-1","volume":"52","author":"AI Kadhim","year":"2019","unstructured":"Kadhim AI (2019) Survey on supervised machine learning techniques for automatic text classification. Artif Intell Rev 52(1):273\u2013292","journal-title":"Artif Intell Rev"},{"key":"8306_CR29","first-page":"4","volume":"8","author":"AA Khan","year":"2021","unstructured":"Khan AA, Laghari AA, Awan SA (2021) Machine learning in computer vision: a review. EAI Trans Scalable Inf Syst 8:4","journal-title":"EAI Trans Scalable Inf Syst"},{"key":"8306_CR30","first-page":"1","volume":"19","author":"X Li","year":"2021","unstructured":"Li X, Cao Z, Zhao L, Jiang J (2021) Alpn: Active-learning-based prototypical network for few-shot hyperspectral imagery classification. IEEE Geosci Remote Sens Let 19:1\u20135","journal-title":"IEEE Geosci Remote Sens Let"},{"key":"8306_CR31","first-page":"1","volume":"60","author":"Z Li","year":"2021","unstructured":"Li Z, Liu M, Chen Y, Xu Y, Li W, Du Q (2021) Deep cross-domain few-shot learning for hyperspectral image classification. IEEE Trans Geosci Remote Sens 60:1\u201318","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"8306_CR32","doi-asserted-by":"publisher","unstructured":"Liao W, Pizurica A, Philips W, Pi Y (2010) A fast iterative kernel pca feature extraction for hyperspectral images. In: 2010 IEEE International Conference on Image Processing, pp. 1317\u20131320. https:\/\/doi.org\/10.1109\/ICIP.2010.5651670","DOI":"10.1109\/ICIP.2010.5651670"},{"issue":"3","key":"8306_CR33","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1109\/LGRS.2011.2172185","volume":"9","author":"G Licciardi","year":"2012","unstructured":"Licciardi G, Marpu PR, Chanussot J, Benediktsson JA (2012) Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geosci Remote Sens Lett 9(3):447\u2013451. https:\/\/doi.org\/10.1109\/LGRS.2011.2172185","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"8306_CR34","doi-asserted-by":"crossref","unstructured":"Liu B, Yu X, Yu A, Zhang P, Wan G, Wang R (2019) Deep few-shot learning for hyperspectral image classification. In: IEEE Transactions on Geo science and remote sensing, vol.\u00a057","DOI":"10.1109\/TGRS.2018.2872830"},{"issue":"6","key":"8306_CR35","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1080\/2150704X.2021.1903609","volume":"12","author":"C Ma","year":"2021","unstructured":"Ma C, Mu X, Zhao P, Yan X (2021) Meta-learning based on parameter transfer for few-shot classification of remote sensing scenes. Remote Sens Lett 12(6):531\u2013541","journal-title":"Remote Sens Lett"},{"issue":"6","key":"8306_CR36","doi-asserted-by":"publisher","first-page":"9411","DOI":"10.1007\/s11042-020-10073-7","volume":"80","author":"M Malik","year":"2021","unstructured":"Malik M, Malik MK, Mehmood K, Makhdoom I (2021) Automatic speech recognition: a survey. Multimed Tools Appl 80(6):9411\u20139457","journal-title":"Multimed Tools Appl"},{"key":"8306_CR37","doi-asserted-by":"crossref","unstructured":"Mankolli E, Guliashki V (2020) Machine learning and natural language processing: Review of models and optimization problems. In: International Conference on ICT Innovations, pp. 71\u201386. Springer","DOI":"10.1007\/978-3-030-62098-1_7"},{"key":"8306_CR38","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1109\/TGRS.2019.2908756","volume":"57","author":"S Mei","year":"2019","unstructured":"Mei S, Ji J, Geng Y, Zhang Z, Li X, Du Q (2019) Unsupervised spatial-spectral feature learning by 3d convolutional autoencoder for hyperspectral classification. IEEE Trans Geosci Remote Sens 57:9","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"8","key":"8306_CR39","doi-asserted-by":"publisher","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","volume":"42","author":"F Melgani","year":"2004","unstructured":"Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens 42(8):1778\u20131790. https:\/\/doi.org\/10.1109\/TGRS.2004.831865","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"2","key":"8306_CR40","doi-asserted-by":"publisher","first-page":"183","DOI":"10.26599\/TST.2018.9010043","volume":"24","author":"A Mughees","year":"2019","unstructured":"Mughees A, Tao L (2019) Multiple deep-belief-network-based spectral-spatial classification of hyperspectral images. Tsinghua Sci Technol 24(2):183\u2013194. https:\/\/doi.org\/10.26599\/TST.2018.9010043","journal-title":"Tsinghua Sci Technol"},{"key":"8306_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2021.3085522","volume":"19","author":"D Pal","year":"2021","unstructured":"Pal D, Bundele V, Banerjee B, Jeppu Y (2021) Spn: stable prototypical network for few-shot learning-based hyperspectral image classification. IEEE Geosci Remote Sens Lett 19:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"8306_CR42","doi-asserted-by":"crossref","unstructured":"Pandey SK, Shekhawat HS, Prasanna SM (2019) Deep learning techniques for speech emotion recognition: A review. In: 2019 29th International Conference Radioelektronika (RADIOELEKTRONIKA), pp. 1\u20136. IEEE","DOI":"10.1109\/RADIOELEK.2019.8733432"},{"issue":"4","key":"8306_CR43","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.1109\/JSTARS.2014.2308425","volume":"7","author":"P Quesada-Barriuso","year":"2014","unstructured":"Quesada-Barriuso P, Arg\u00fcello F, Heras DB (2014) Spectral-spatial classification of hyperspectral images using wavelets and extended morphological profiles. IEEE J Select Topics Appl Earth Observ Remote Sens 7(4):1177\u20131185. https:\/\/doi.org\/10.1109\/JSTARS.2014.2308425","journal-title":"IEEE J Select Topics Appl Earth Observ Remote Sens"},{"issue":"12","key":"8306_CR44","doi-asserted-by":"publisher","first-page":"5086","DOI":"10.1109\/JSTARS.2019.2957047","volume":"12","author":"M Rao","year":"2019","unstructured":"Rao M, Tang P, Zhang Z (2019) Spatial-spectral relation network for hyperspectral image classification with limited training samples. IEEE J Select Topics Appl Earth Observ Remote Sens 12(12):5086\u20135100","journal-title":"IEEE J Select Topics Appl Earth Observ Remote Sens"},{"key":"8306_CR45","doi-asserted-by":"crossref","unstructured":"Rashed BM, Popescu N (2021) Machine learning techniques for medical image processing. In: 2021 International Conference on e-Health and Bioengineering (EHB), pp. 1\u20134. IEEE","DOI":"10.1109\/EHB52898.2021.9657673"},{"key":"8306_CR46","doi-asserted-by":"publisher","unstructured":"Ren Y, Zhang Y, wei W, Li L (2014) A spectral-spatial hyperspectral data classification approach using random forest with label constraints. In: 2014 IEEE Workshop on Electronics, Computer and Applications, pp. 344\u2013347. https:\/\/doi.org\/10.1109\/IWECA.2014.6845627","DOI":"10.1109\/IWECA.2014.6845627"},{"issue":"1","key":"8306_CR47","doi-asserted-by":"publisher","first-page":"97","DOI":"10.3390\/electronics9010097","volume":"9","author":"R Sagar","year":"2020","unstructured":"Sagar R, Jhaveri R, Borrego C (2020) Applications in security and evasions in machine learning: a survey. Electronics 9(1):97","journal-title":"Electronics"},{"key":"8306_CR48","doi-asserted-by":"crossref","unstructured":"Sanghvi K, Aralkar A, Sanghvi S, Saha I (2020) A survey on image classification techniques. Available at SSRN 3754116","DOI":"10.2139\/ssrn.3754116"},{"key":"8306_CR49","unstructured":"Song Y, Wang T, Mondal SK, Sahoo JP (2022) A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities. arXiv preprint arXiv:2205.06743"},{"issue":"2","key":"8306_CR50","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/S0034-4257(01)00299-1","volume":"80","author":"IB Strachan","year":"2002","unstructured":"Strachan IB, Pattey E, Boisvert JB (2002) Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance. Remote Sens Environ 80(2):213\u2013224. https:\/\/doi.org\/10.1016\/S0034-4257(01)00299-1","journal-title":"Remote Sens Environ"},{"key":"8306_CR51","doi-asserted-by":"crossref","unstructured":"Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C (2018) A survey on deep transfer learning. In: International conference on artificial neural networks, pp. 270\u2013279. Springer","DOI":"10.1007\/978-3-030-01424-7_27"},{"issue":"1","key":"8306_CR52","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1109\/LGRS.2019.2916083","volume":"17","author":"H Tang","year":"2019","unstructured":"Tang H, Li Y, Han X, Huang Q, Xie W (2019) A spatial-spectral prototypical network for hyperspectral remote sensing image. IEEE Geosci Remote Sens Lett 17(1):167\u2013171","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"8306_CR53","doi-asserted-by":"crossref","unstructured":"Tong X, Yin J, Han B, Qv H (2020) Few-shot learning with attention-weighted graph convolutional networks for hyperspectral image classification. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 1686\u20131690. IEEE","DOI":"10.1109\/ICIP40778.2020.9190752"},{"key":"8306_CR54","doi-asserted-by":"crossref","unstructured":"Torrey L, Shavlik J (2010) Transfer learning. In: Handbook of research on machine learning applications and trends: algorithms, methods, and techniques, pp. 242\u2013264. IGI global","DOI":"10.4018\/978-1-60566-766-9.ch011"},{"key":"8306_CR55","doi-asserted-by":"publisher","DOI":"10.1186\/s13634-016-0355-x","author":"RVB Vangara","year":"2020","unstructured":"Vangara RVB, Vangara SP, Thirupathur V (2020) A survey on natural language processing in context with machine learning. Int J Anal Exp Modal Anal. https:\/\/doi.org\/10.1186\/s13634-016-0355-x","journal-title":"Int J Anal Exp Modal Anal"},{"key":"8306_CR56","doi-asserted-by":"crossref","unstructured":"Wang G, Zheng X, Cheng L, Wan X, Guo Z (2021) Hyperspectral image classification based on improved few shot learning. In: 2021 IEEE International Conference on Electronic Technology, Communication and Information (ICETCI), pp. 673\u2013676. IEEE","DOI":"10.1109\/ICETCI53161.2021.9563257"},{"key":"8306_CR57","doi-asserted-by":"publisher","unstructured":"Wang S (2020) hyperspectral dataset. IEEE Dataport. https:\/\/doi.org\/10.21227\/eqk7-wa46. https:\/\/dx.doi.org\/10.21227\/eqk7-wa46","DOI":"10.21227\/eqk7-wa46"},{"key":"8306_CR58","first-page":"1","volume":"60","author":"S Wang","year":"2021","unstructured":"Wang S, Du B, Zhang D, Wan F (2021) Adversarial prototype learning for hyperspectral image classification. IEEE Trans Geosci Remote Sens 60:1\u20138","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"8306_CR59","first-page":"1","volume":"19","author":"Y Wang","year":"2021","unstructured":"Wang Y, Liu M, Yang Y, Li Z, Du Q, Chen Y, Li F, Yang H (2021) Heterogeneous few-shot learning for hyperspectral image classification. IEEE Geosci Remote Sens Lett 19:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"1","key":"8306_CR60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss K, Khoshgoftaar TM, Wang D (2016) A survey of transfer learning. J Big Data 3(1):1\u201340","journal-title":"J Big Data"},{"key":"8306_CR61","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.neucom.2016.09.010","volume":"219","author":"S Yu","year":"2017","unstructured":"Yu S, Jia S, Xu C (2017) Convolutional neural networks for hyperspectral image classification. Neurocomputing 219:88\u201398. https:\/\/doi.org\/10.1016\/j.neucom.2016.09.010","journal-title":"Neurocomputing"},{"issue":"4","key":"8306_CR62","doi-asserted-by":"publisher","first-page":"647","DOI":"10.3390\/rs12040647","volume":"12","author":"C Zhang","year":"2020","unstructured":"Zhang C, Yue J, Qin Q (2020) Deep quadruplet network for hyperspectral image classification with a small number of samples. Remote Sens 12(4):647","journal-title":"Remote Sens"},{"key":"8306_CR63","doi-asserted-by":"publisher","first-page":"4748","DOI":"10.1109\/JSTARS.2020.3017544","volume":"13","author":"C Zhang","year":"2020","unstructured":"Zhang C, Yue J, Qin Q (2020) Global prototypical network for few-shot hyperspectral image classification. IEEE J Select Topics Appl Earth Observ Remote Sens 13:4748\u20134759","journal-title":"IEEE J Select Topics Appl Earth Observ Remote Sens"},{"issue":"1","key":"8306_CR64","doi-asserted-by":"publisher","first-page":"108","DOI":"10.3390\/rs13010108","volume":"13","author":"P Zhang","year":"2021","unstructured":"Zhang P, Bai Y, Wang D, Bai B, Li Y (2021) Few-shot classification of aerial scene images via meta-learning. Remote Sens 13(1):108","journal-title":"Remote Sens"},{"key":"8306_CR65","doi-asserted-by":"crossref","unstructured":"Zhang Y, Li W, Zhang M, Tao R (2022) Dual graph cross-domain few-shot learning for hyperspectral image classification. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3573\u20133577. IEEE","DOI":"10.1109\/ICASSP43922.2022.9747622"},{"key":"8306_CR66","doi-asserted-by":"crossref","unstructured":"Zheng C, Zheng Y (2014) Hyperspectral remote sensing image classification based on combined svm and lda. SPIE Asia Pac. Remote Sens","DOI":"10.1117\/12.2070688"},{"key":"8306_CR67","doi-asserted-by":"publisher","first-page":"847","DOI":"10.1109\/TGRS.2017.2755542","volume":"56","author":"Z Zhong","year":"2017","unstructured":"Zhong Z, Li J, Luo Z, Chapman M (2017) Spectral-spatial residual network for hyperspectral image classification: a 3-d deep learning framework. IEEE Trans Geosci Remote Sens 56:847\u2013858. https:\/\/doi.org\/10.1109\/TGRS.2017.2755542","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"1","key":"8306_CR68","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2020","unstructured":"Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, Xiong H, He Q (2020) A comprehensive survey on transfer learning. Proc IEEE 109(1):43\u201376","journal-title":"Proc IEEE"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08306-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08306-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08306-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T00:23:45Z","timestamp":1682382225000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08306-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,4]]},"references-count":68,"journal-issue":{"issue":"15","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["8306"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08306-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,4]]},"assertion":[{"value":"16 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 February 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare no conflict of interest in the research findings presented in this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}