{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T19:25:58Z","timestamp":1768073158460,"version":"3.49.0"},"reference-count":60,"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:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Key Research and Development Projects of Hunan Province","award":["2020SK2134"],"award-info":[{"award-number":["2020SK2134"]}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2022JJ30625"],"award-info":[{"award-number":["2022JJ30625"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014472","name":"Scientific Research Project of Hunan Provincial Department of Education","doi-asserted-by":"publisher","award":["20C1249"],"award-info":[{"award-number":["20C1249"]}],"id":[{"id":"10.13039\/100014472","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Geosci. Remote Sensing"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tgrs.2023.3313800","type":"journal-article","created":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T18:06:15Z","timestamp":1694455575000},"page":"1-13","source":"Crossref","is-referenced-by-count":5,"title":["BFRNet: Bidimensional Feature Representation Network for Remote Sensing Images Classification"],"prefix":"10.1109","volume":"61","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2298-3429","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8951-6733","authenticated-orcid":false,"given":"Ting","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2386-5405","authenticated-orcid":false,"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ji","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3042276"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3241193"},{"key":"ref56","first-page":"10096","article-title":"EfficientNetv2: Smaller models and faster training","author":"tan","year":"2021","journal-title":"Proc Conf Mach Learn (CMLR)"},{"key":"ref15","article-title":"MAFormer: A transformer network with multi-scale attention fusion for visual recognition","author":"wang","year":"2022","journal-title":"arXiv 2209 01620"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3194505"},{"key":"ref14","first-page":"1","article-title":"ResT: An efficient transformer for visual recognition","author":"zhang","year":"2021","journal-title":"Proc Neural Inf Process Syst (NeurIPS)"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2022.3141826"},{"key":"ref53","article-title":"Multi-scale attention network for single image super-resolution","author":"wang","year":"2022","journal-title":"arXiv 2209 14145"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3202765"},{"key":"ref11","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","author":"tan","year":"2019","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref55","article-title":"LightViT: Towards light-weight convolution-free vision transformers","author":"huang","year":"2022","journal-title":"arXiv 2207 05557"},{"key":"ref10","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"howard","year":"2017","journal-title":"arXiv 1704 04861"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref17","first-page":"1","article-title":"HorNet: Efficient high-order spatial interactions with recursive gated convolutions","author":"rao","year":"2022","journal-title":"Proc Conf Neural Inf Process Syst (NeurIPS)"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2022.3198517"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3144158"},{"key":"ref18","first-page":"3965","article-title":"CoAtNet: Marrying convolution and attention for all data sizes","author":"dai","year":"2021","journal-title":"Proc Conf Neural Inf Process Syst (NeurIPS)"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2389824"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00518"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00082"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3144894"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00314"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref42","first-page":"3","article-title":"EdgeNeXt: Efficiently amalgamated CNN-transformer architecture for mobile vision applications","author":"maaz","year":"2022","journal-title":"Proc Eur Conf Comput Vis (ECCV)"},{"key":"ref41","first-page":"6000","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"Proc Int Conf Neural Inf Process Syst (NIPS)"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"ref49","article-title":"Gaussian error linear units (GELUs)","author":"hendrycks","year":"2016","journal-title":"arXiv 1606 08415"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3016820"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3001401"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00060"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3124913"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref5","article-title":"An image is worth 16×16 words: Transformers for image recognition at scale","author":"dosovitskiy","year":"2020","journal-title":"arXiv 2010 11929"},{"key":"ref40","article-title":"Focal self-attention for local–global interactions in vision transformers","author":"yang","year":"2021","journal-title":"arXiv 2107 00641"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"ref34","first-page":"9355","article-title":"Twins: Revisiting the design of spatial attention in vision transformers","author":"chu","year":"2021","journal-title":"Proc Neural Inf Process Syst (NeurIPS)"},{"key":"ref37","article-title":"Shuffle transformer: Rethinking spatial shuffle for vision transformer","author":"huang","year":"2021","journal-title":"arXiv 2106 03650"},{"key":"ref36","article-title":"PVT v2: Improved baselines with pyramid vision transformer","author":"wang","year":"2021","journal-title":"arXiv 2106 13797"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00675"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3130716"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01172"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3235988"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2022.3187135"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01175"},{"key":"ref38","article-title":"Degenerate Swin to win: Plain window-based transformer without sophisticated operations","author":"yu","year":"2022","journal-title":"arXiv 2211 14255"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref23","article-title":"MobileViT: Light-weight, general-purpose, and mobile-friendly vision transformer","author":"mehta","year":"2021","journal-title":"arXiv 2110 02178"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.3390\/rs10040568"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2022.3173349"},{"key":"ref20","article-title":"Global context vision transformers","author":"hatamizadeh","year":"2022","journal-title":"arXiv 2206 09959"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00520"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20053-3_27"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3144165"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3169018"},{"key":"ref29","first-page":"10347","article-title":"Training data-efficient image transformers & distillation through attention","author":"touvron","year":"2021","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3229039"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/36\/10006360\/10247060.pdf?arnumber=10247060","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T18:08:13Z","timestamp":1698084493000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10247060\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":60,"URL":"https:\/\/doi.org\/10.1109\/tgrs.2023.3313800","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"value":"0196-2892","type":"print"},{"value":"1558-0644","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}