{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T02:10:10Z","timestamp":1769739010754,"version":"3.49.0"},"reference-count":54,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Creating the Dataset","award":["ONR-G N62909-18-1-2169"],"award-info":[{"award-number":["ONR-G N62909-18-1-2169"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972321"],"award-info":[{"award-number":["61972321"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research and Development Plan of Shaanxi Province","award":["2017ZDXM-GY-094"],"award-info":[{"award-number":["2017ZDXM-GY-094"]}]},{"name":"Research and Development Plan of Shaanxi Province","award":["2015KTZDGY04-01"],"award-info":[{"award-number":["2015KTZDGY04-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/jstars.2020.3043109","type":"journal-article","created":{"date-parts":[[2020,12,9]],"date-time":"2020-12-09T04:10:50Z","timestamp":1607487050000},"page":"1705-1716","source":"Crossref","is-referenced-by-count":32,"title":["<i>DRL<\/i>-GAN: Dual-Stream Representation Learning GAN for Low-Resolution Image Classification in UAV Applications"],"prefix":"10.1109","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3689-1621","authenticated-orcid":false,"given":"Yue","family":"Xi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0940-3338","authenticated-orcid":false,"given":"Wenjing","family":"Jia","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0623-584X","authenticated-orcid":false,"given":"Jiangbin","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Xiaochen","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Yefan","family":"Xie","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6116-3194","authenticated-orcid":false,"given":"Jinchang","family":"Ren","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8962-540X","authenticated-orcid":false,"given":"Xiangjian","family":"He","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00129"},{"key":"ref38","article-title":"Frequency-aware reconstruction of fluid simulations with generative networks","author":"biland","year":"0","journal-title":"arXiv 1912 08776"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00019"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00982"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2018.2810121"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.01.013"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00356"},{"key":"ref36","article-title":"Towards understanding the spectral bias of deep learning","author":"cao","year":"0","journal-title":"arXiv 1912 01198"},{"key":"ref35","author":"xu","year":"0","journal-title":"arXiv 1808 04295"},{"key":"ref34","article-title":"Frequency principle: Fourier analysis sheds light on deep neural networks","author":"xu","year":"2019","journal-title":"arXiv 1901 06523"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.02.004"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2015.2398468"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.518"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00418"},{"key":"ref1","first-page":"375","article-title":"The unmanned aerial vehicle benchmark: Object detection and tracking","author":"du","year":"0","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2019.2925456"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33018215"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/124"},{"key":"ref24","volume":"19","author":"padfield","year":"0","journal-title":"SENSORS"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00818"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2874715"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2014.04.006"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2019.03.009"},{"key":"ref51","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2902431"},{"key":"ref53","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","author":"tan","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00420"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00262"},{"key":"ref40","first-page":"1138","article-title":"Towards domain adaptive vehicle detection in satellite image by supervised super-resolution transfer","volume":"35","author":"cao","year":"0","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00167"},{"key":"ref13","first-page":"264","article-title":"Training behavior of deep neural network in frequency domain","author":"xu","year":"0","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref14","first-page":"5301","article-title":"On the spectral bias of neural networks","author":"rahaman","year":"0","journal-title":"Proc 36th Int Conf Mach Learn"},{"key":"ref15","first-page":"206","article-title":"SOD-MTGAN: Small object detection via multi-task generative adversarial network","author":"bai","year":"0","journal-title":"Proc IEEE Eur Conf Comput Vis"},{"key":"ref16","first-page":"6967","author":"jiao","year":"0","journal-title":"Proc 32nd AAAI Conf Artif Intell"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/541"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"2503","DOI":"10.1109\/TCSVT.2019.2925844","article-title":"Multi-temporal ultra dense memory network for video super-resolution","volume":"30","author":"yi","year":"2020","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref3","first-page":"7315","article-title":"Extreme low resolution activity recognition with multi-siamese embedding learning","author":"ryoo","year":"0","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2011.07.016"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2019.02.005"},{"key":"ref49","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref9","first-page":"63","article-title":"ESRGAN: Enhanced super-resolution generative adversarial networks","author":"wang","year":"0","journal-title":"Proc IEEE Eur Conf Comput Vis"},{"key":"ref46","first-page":"6722","article-title":"Learning a wavelet-like auto-encoder to accelerate deep neural networks","author":"chen","year":"0","journal-title":"Proc 32nd AAAI Conf Artif Intell"},{"key":"ref45","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"hinton","year":"2006","journal-title":"Science"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2016.2565705"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2978980"},{"key":"ref42","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"radford","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2908802"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2011.2109730"},{"key":"ref43","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"0","journal-title":"Proc Conf Neural Inf Proc Syst"}],"container-title":["IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/4609443\/9314330\/09286580.pdf?arnumber=9286580","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T16:39:48Z","timestamp":1643215188000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9286580\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":54,"URL":"https:\/\/doi.org\/10.1109\/jstars.2020.3043109","relation":{},"ISSN":["1939-1404","2151-1535"],"issn-type":[{"value":"1939-1404","type":"print"},{"value":"2151-1535","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}