{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T12:05:47Z","timestamp":1777982747878,"version":"3.51.4"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T00:00:00Z","timestamp":1622419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T00:00:00Z","timestamp":1622419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1007\/s12145-021-00634-1","type":"journal-article","created":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T07:26:17Z","timestamp":1622445977000},"page":"1327-1337","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Unsupervised automatic classification of all-sky auroral images using deep clustering technology"],"prefix":"10.1007","volume":"14","author":[{"given":"Qiuju","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jimin","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,31]]},"reference":[{"issue":"4","key":"634_CR1","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/0032-0633(64)90151-5","volume":"12","author":"SI Akasofu","year":"1964","unstructured":"Akasofu SI (1964) The development of the auroral substorm. Planet Space Sci 12(4):273\u2013282","journal-title":"Planet Space Sci"},{"key":"634_CR2","doi-asserted-by":"publisher","first-page":"1885","DOI":"10.1016\/0021-9169(95)00180-8","volume":"58","author":"M Ayukawa","year":"1996","unstructured":"Ayukawa M, Makita K, Yamagishi H, Ejiri M, Sakanoi T (1996) Characteristics of polar cap aurora. J Atmos Terr Phys 58:1885\u20131894","journal-title":"J Atmos Terr Phys"},{"key":"634_CR3","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1002\/vis.304","volume":"14","author":"GVG Baranoski","year":"2003","unstructured":"Baranoski GVG, Rokne JG, Shirley P, Trondsen TS, Rui B (2003) Simulating the aurora. J Vis Comput Animat 14:43\u201359","journal-title":"J vis Comput Animat"},{"issue":"7","key":"634_CR4","first-page":"1","volume":"1","author":"C Biradar","year":"2012","unstructured":"Biradar C, Pratiksha SB (2012) An Innovative Approach for Aurora Recognition. International Journal of Engineering Research and Technology 1(7):1\u20135","journal-title":"International Journal of Engineering Research and Technology"},{"key":"634_CR5","doi-asserted-by":"crossref","unstructured":"Chopra S, Hadsell R, Lecun Y (2005) Learning a similarity metric discriminatively, with application to face verification. In Conference on Computer Vision and Pattern Recognition (CVPR'05), San Diego, CA, 539\u2013546","DOI":"10.1109\/CVPR.2005.202"},{"key":"634_CR6","doi-asserted-by":"publisher","first-page":"5640","DOI":"10.1029\/2018JA025274","volume":"123","author":"LBN Clausen","year":"2018","unstructured":"Clausen LBN, Nickisch H (2018) Automatic classification of auroral images from the Oslo Auroral THEMIS (OATH) dataset using machine learning. J Geophys Res Space Physics 123:5640\u20135647","journal-title":"J Geophys Res Space Physics"},{"issue":"1","key":"634_CR7","doi-asserted-by":"publisher","first-page":"81","DOI":"10.5194\/hgss-5-81-2014","volume":"5","author":"YI Feldstein","year":"2014","unstructured":"Feldstein YI, Vorobjev VG, Zverev VL, Forster M (2014) Investigations of the auroral luminosity distribution and the dynamics of discrete auroral forms in a historical retrospective. Hist Geo Space Sci 5(1):81\u2013135","journal-title":"Hist Geo Space Sci"},{"key":"634_CR8","doi-asserted-by":"publisher","first-page":"7447","DOI":"10.1002\/2015JA021699","volume":"120","author":"D Han","year":"2015","unstructured":"Han D, Chen X, Liu J et al (2015) An extensive survey of dayside diffuse aurora based on optical observations at Yellow River Station. J Geophys Res Space Physics 120:7447\u20137465","journal-title":"J Geophys Res Space Physics"},{"key":"634_CR9","first-page":"1735","volume":"2","author":"R Hadsell","year":"2006","unstructured":"Hadsell R, Chopra S, LeCun Y (2006) Dimensionality reduction by learning an invariant mapping. IEEE Conference on Computer Vision and Pattern Recognition 2:1735\u20131742","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition"},{"issue":"1","key":"634_CR10","first-page":"100","volume":"28","author":"JA Hartigan","year":"1979","unstructured":"Hartigan JA, Wong MA (1979) Algorithm AS 136: A K-Means Clustering Algorithm. J R Stat Soc Ser C 28(1):100\u2013108","journal-title":"J R Stat Soc Ser C"},{"key":"634_CR11","first-page":"8","volume":"11","author":"H Hu","year":"1999","unstructured":"Hu H, Liu R, Wang J et al (1999) Statistic characteristics of the aurora observed at Zhongshan Station. Antarctica. Chinese Journal of Polar Research 11:8\u201318","journal-title":"Antarctica Chinese Journal of Polar Research"},{"issue":"8","key":"634_CR12","doi-asserted-by":"publisher","first-page":"794","DOI":"10.1016\/j.jastp.2009.02.010","volume":"71","author":"ZJ Hu","year":"2009","unstructured":"Hu ZJ, Yang H, Huang D et al (2009) Synoptic distribution of dayside aurora: multiple-wavelength all-sky observation at Yellow River Station in Ny-\u00c5lesund, Svalbard. J Atmos Solar Terr Phys 71(8):794\u2013804","journal-title":"J Atmos Solar Terr Phys"},{"key":"634_CR13","first-page":"2579","volume":"9","author":"L Maaten","year":"2008","unstructured":"Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579\u20132605","journal-title":"J Mach Learn Res"},{"key":"634_CR14","first-page":"1","volume":"99","author":"C Niu","year":"2018","unstructured":"Niu C, Zhang J, Wang Q, Liang J (2018) Weakly supervised semantic segmentation for joint key local structure localization and classification of aurora image. IEEE Trans Geosci Remote Sens 99:1\u201314","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"634_CR15","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323:533\u2013536","journal-title":"Nature"},{"issue":"A10","key":"634_CR16","doi-asserted-by":"publisher","first-page":"23325","DOI":"10.1029\/98JA02156","volume":"103","author":"PE Sandholt","year":"1998","unstructured":"Sandholt PE, Farrugia CJ, Moen J et al (1998) A classification of dayside auroral forms and activities as a function of interplanetary magnetic field orientation. J Geophys Res Space Physics 103(A10):23325\u201323345","journal-title":"J Geophys Res Space Physics"},{"key":"634_CR17","unstructured":"Shaham U, Stanton K, Li H (2018) Spectralnet: Spectral clustering using deep neural networks. In International Conference on Learning Representations(ICLR2018), Vancouver, Canada, 1\u201320"},{"key":"634_CR18","first-page":"247","volume":"108","author":"D Simmons","year":"1998","unstructured":"Simmons D (1998) A classification of auroral types. J Br Astron Assoc 108:247\u2013257","journal-title":"J Br Astron Assoc"},{"key":"634_CR19","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. The 3rd International Conference on Learning Representations (ICLR2015), San Diego, CA, 1\u201314"},{"key":"634_CR20","unstructured":"Steen \u00c5, Br\u00e4ndstr\u00f6m U, Gustavsson B, Aso T (1997) ALIS- a multi-station imaging system at high latitudes with multi-disciplinary scientific objectives.\u00a0European Rocket & Balloon Programmes & Related Research, 261\u2013266"},{"key":"634_CR21","volume-title":"The Polar Aurora","author":"C St\u00f6rmer","year":"1955","unstructured":"St\u00f6rmer C (1955) The Polar Aurora. Clarendon Press, Oxford"},{"key":"634_CR22","doi-asserted-by":"crossref","unstructured":"Sun N, Yu H (2018) A method to determine the number of clusters based on multi-validity index. International Joint Conference on Rough Sets. Springer, Cham","DOI":"10.1007\/978-3-319-99368-3_33"},{"key":"634_CR23","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.5194\/angeo-22-1103-2004","volume":"22","author":"MT Syrj\u00e4suo","year":"2004","unstructured":"Syrj\u00e4suo MT, Donovan EF (2004) Diurnal auroral occurrence statistics obtained via machine vision. Ann Geophys 22:1103\u20132113","journal-title":"Ann Geophys"},{"key":"634_CR24","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.eswa.2019.01.074","volume":"125","author":"R \u00dcnl\u00fc","year":"2019","unstructured":"\u00dcnl\u00fc R, Xanthopoulos P (2019) Estimating the number of clusters in a dataset via consensus clustering. Expert Syst Appl 125:33\u201339","journal-title":"Expert Syst Appl"},{"key":"634_CR25","doi-asserted-by":"crossref","unstructured":"Wang Q, Liang J, Hu ZJ et al (2010) Spatial texture based automatic classification of dayside aurora in all-sky images. J Atmos Sol Terr Phys, 72(5):498\u2013508","DOI":"10.1016\/j.jastp.2010.01.011"},{"key":"634_CR26","unstructured":"Xie J, Girshick R, Farhadi A (2016) Unsupervised deep embedding for clustering analysis. Proceedings of the 33rd International Conference on Machine Learning"},{"issue":"9","key":"634_CR27","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1016\/S1364-6826(00)00054-7","volume":"62","author":"H Yang","year":"2000","unstructured":"Yang H, Sato N, Makita K et al (2000) Synoptic observations of auroras along the postnoon oval: a survey with all-sky TV observations at zhongshan, antarctica. J Atmos Solar Terr Phys 62(9):787\u2013797","journal-title":"J Atmos Solar Terr Phys"},{"issue":"12","key":"634_CR28","doi-asserted-by":"publisher","first-page":"5049","DOI":"10.1109\/TGRS.2012.2195667","volume":"50","author":"Q Yang","year":"2012","unstructured":"Yang Q, Liang J, Hu Z, Zhao H (2012) Auroral sequence representation and classification using hidden markov models. IEEE Trans Geosci Remote Sens 50(12):5049\u20135060","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"634_CR29","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1109\/JSTARS.2020.2969245","volume":"13","author":"Q Yang","year":"2020","unstructured":"Yang Q, Zhou P (2020) Representation and classification of auroral images based on convolutional neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13:523\u2013534","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"634_CR30","first-page":"1","volume":"99","author":"J Zhang","year":"2019","unstructured":"Zhang J, Liu M, Lu K, Gao Y (2019) Group-wise learning for aurora image classification with multiple representations. IEEE Transactions on Cybernetics 99:1\u201313","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"2","key":"634_CR31","doi-asserted-by":"publisher","first-page":"233","DOI":"10.3390\/rs10020233","volume":"10","author":"Y Zhong","year":"2018","unstructured":"Zhong Y, Huang R, Zhao J, Zhao B, Liu T (2018) Aurora image classification based on multi-feature latent dirichlet allocation. Remote Sensing 10(2):233\u2013249","journal-title":"Remote Sensing"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-021-00634-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-021-00634-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-021-00634-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T23:59:28Z","timestamp":1672271968000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-021-00634-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,31]]},"references-count":31,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["634"],"URL":"https:\/\/doi.org\/10.1007\/s12145-021-00634-1","relation":{"has-preprint":[{"id-type":"doi","id":"10.1002\/essoar.10503300.1","asserted-by":"object"}]},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,31]]},"assertion":[{"value":"20 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 May 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}