{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T19:10:34Z","timestamp":1791486634228,"version":"4.3.4"},"reference-count":71,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61725105"],"award-info":[{"award-number":["61725105"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076241"],"award-info":[{"award-number":["62076241"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/jstars.2023.3263149","type":"journal-article","created":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T17:29:51Z","timestamp":1680197391000},"page":"4591-4606","source":"Crossref","is-referenced-by-count":19,"title":["TDNet: A Novel Transductive Learning Framework With Conditional Metric Embedding for Few-Shot Remote Sensing Image Scene Classification"],"prefix":"10.1109","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0604-3728","authenticated-orcid":false,"given":"Bing","family":"Wang","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2877-0384","authenticated-orcid":false,"given":"Zhirui","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0038-9816","authenticated-orcid":false,"given":"Xian","family":"Sun","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2158-559X","authenticated-orcid":false,"given":"Qibin","family":"He","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongqi","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8647-9627","authenticated-orcid":false,"given":"Kun","family":"Fu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2020.3005403"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.2991657"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3016020"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3149394"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3097093"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2022.3169773"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3093334"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.5555\/3294996.3295163"},{"key":"ref9","first-page":"741","volume-title":"Proc. 16th Eur. Conf. Comput. Vis.","author":"Liu","year":"2020"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-020-3156-7"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01176"},{"key":"ref12","first-page":"1","article-title":"Meta-learning with warped gradient descent","volume-title":"Proc. 8th Int. Conf. Learn. Representations","author":"Flennerhag","year":"2020"},{"key":"ref13","first-page":"10617","article-title":"MetaFun: Meta-learning with iterative functional updates","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Xu"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00844"},{"key":"ref15","first-page":"1","article-title":"Free lunch for few-shot learning: Distribution calibration","volume-title":"Proc. 9th Int. Conf. Learn. Representations","author":"Yang","year":"2021"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00929"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3193587"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00789"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00851"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3200056"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3184080"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3109268"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2021.3052869"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3185795"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2020.3027387"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2021.3099033"},{"key":"ref27","first-page":"2579","article-title":"Visualizing data using T-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/bf00130487"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2002.1017623"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1023\/b:visi.0000029664.99615.94"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2004.1315206"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3020424"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3192321"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3047447"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2016.07.001"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2017.2783902"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2019.2931801"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2018.2864987"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2021.3127851"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2006.79"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/3386252"},{"issue":"2","key":"ref42","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1023\/A:1019956318069","article-title":"A perspective view and survey of meta-learning","volume":"18","author":"Vilalta","year":"2002","journal-title":"Artif. Intell. Rev"},{"key":"ref43","first-page":"3630","article-title":"Matching networks for one shot learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Vinyals","year":"2016"},{"key":"ref44","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Finn","year":"2017"},{"key":"ref45","article-title":"Meta-SGD: Learning to learn quickly for few shot learning","author":"Li","year":"2017"},{"key":"ref46","article-title":"On first-order meta-learning algorithms","author":"Nichol","year":"2018"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/iccv.2017.328"},{"key":"ref48","first-page":"1","article-title":"Bowtie networks: Generative modeling for joint few-shot recognition and novel-view synthesis","volume-title":"Proc. 9th Int. Conf. Learn. Representations","author":"Bao","year":"2021"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2020.3033336"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2022.3153679"},{"key":"ref51","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-01548-9","author":"Zhu","year":"2009","journal-title":"Introduction to Semi-Supervised Learn."},{"key":"ref52","first-page":"1163","article-title":"Regularization with stochastic transformations and perturbations for deep semi-supervised learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Sajjadi","year":"2016"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2021.10.008"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01228-1_19"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00521"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.3390\/rs13040569"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.3390\/rs12213603"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.3390\/rs11091038"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2022.3140485"},{"key":"ref60","first-page":"896","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume-title":"Proc. Workshop Challenges Represent. Learn.","author":"Lee","year":"2013"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3195924"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2018.2888485"},{"key":"ref63","first-page":"265","article-title":"On the algorithmic implementation of multiclass kernel-based vector machines","volume":"2","author":"Crammer","year":"2001","journal-title":"J. Mach. Learn. Res"},{"key":"ref64","first-page":"136","article-title":"OptNet: Differentiable optimization as a layer in neural networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Amos","year":"2017"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/jproc.2017.2675998"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2016.2645610"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2685945"},{"key":"ref68","first-page":"5905","article-title":"ASAM: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Kwon","year":"2021"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58548-8_26"},{"key":"ref71","first-page":"1","article-title":"A closer look at few-shot classification","volume-title":"Proc. 7th Int. Conf. Learn. Representations","author":"Chen","year":"2019"}],"container-title":["IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/4609443\/9973430\/10087351.pdf?arnumber=10087351","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T03:42:35Z","timestamp":1710387755000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10087351\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":71,"URL":"https:\/\/doi.org\/10.1109\/jstars.2023.3263149","relation":{},"ISSN":["1939-1404","2151-1535"],"issn-type":[{"value":"1939-1404","type":"print"},{"value":"2151-1535","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}