{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T12:05:05Z","timestamp":1771329905375,"version":"3.50.1"},"reference-count":63,"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":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62036006"],"award-info":[{"award-number":["62036006"]}],"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":["62133012"],"award-info":[{"award-number":["62133012"]}],"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":["61936006"],"award-info":[{"award-number":["61936006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovation Capability Support Program of Shaanxi","award":["2021TD-05"],"award-info":[{"award-number":["2021TD-05"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Geosci. Remote Sensing"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tgrs.2023.3277077","type":"journal-article","created":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T13:26:13Z","timestamp":1684329973000},"page":"1-14","source":"Crossref","is-referenced-by-count":20,"title":["Energy-Based CNN Pruning for Remote Sensing Scene Classification"],"prefix":"10.1109","volume":"61","author":[{"given":"Yiheng","family":"Lu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi’an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0415-8556","authenticated-orcid":false,"given":"Maoguo","family":"Gong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi’an, China"}]},{"given":"Zhuping","family":"Hu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi’an, China"}]},{"given":"Wei","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi’an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2413-4698","authenticated-orcid":false,"given":"Ziyu","family":"Guan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi’an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9768-516X","authenticated-orcid":false,"given":"Mingyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi’an, China"}]}],"member":"263","reference":[{"key":"ref13","first-page":"263","article-title":"Optimal brain surgeon","author":"hassibi","year":"1993","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref57","first-page":"1","article-title":"Rethinking the value of network pruning","author":"liu","year":"2019","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref12","first-page":"598","article-title":"Optimal brain damage","author":"lecun","year":"1990","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.3390\/rs14010141"},{"key":"ref15","article-title":"Pruning convolutional neural networks for resource efficient inference","author":"molchanov","year":"2016","journal-title":"arXiv 1611 06440"},{"key":"ref59","first-page":"1","article-title":"The early phase of neural network training","author":"frankle","year":"2020","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref14","first-page":"2498","article-title":"Variational dropout sparsifies deep neural networks","author":"molchanov","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref58","first-page":"1","article-title":"Rethinking the pruning criteria for convolutional neural network","author":"huang","year":"2021","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2020.09.034"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR56361.2022.9956372"},{"key":"ref11","first-page":"1","article-title":"DARTS: Differentiable architecture search","author":"liu","year":"2018","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3063287"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2494218"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3020424"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.541"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.298"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2502579"},{"key":"ref18","first-page":"1","article-title":"Convolutional neural networks with low-rank regularization","author":"tai","year":"2016","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.155"},{"key":"ref50","first-page":"1","article-title":"Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers","author":"ye","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2913816"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2848473"},{"key":"ref48","first-page":"1","article-title":"The lottery ticket hypothesis: Finding sparse, trainable neural networks","author":"frankle","year":"2019","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2909695"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2917161"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2845668"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1117\/1.JRS.10.025006"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2015.2488681"},{"key":"ref49","article-title":"Pruning filters for efficient ConvNets","author":"li","year":"2017","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref8","first-page":"5048","article-title":"Aging evolution for image classifier architecture search","author":"real","year":"2019","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3234252"},{"key":"ref9","first-page":"1","article-title":"Neural architecture search with reinforcement learning","author":"zoph","year":"2017","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2931801"},{"key":"ref3","first-page":"865","article-title":"Traffic flow prediction with big data: A deep learning approach","volume":"16","author":"lv","year":"2015","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3130940"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2920374"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2700322"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.3390\/rs11171996"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2019.2894399"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3238327"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3197445"},{"key":"ref31","first-page":"1106","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2019.04.015"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref32","first-page":"1646","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2011.2165548"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2010.2043109"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2906883"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.3390\/rs11050494"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2010.2055033"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2018.01.004"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2011.2139193"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2449668"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3070853"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3001401"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2685945"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2017.2675998"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2765364"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2016.2616418"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2907932"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3097938"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2020.11.025"},{"key":"ref61","article-title":"Convolution neural network architecture learning for remote sensing scene classification","author":"chen","year":"2020","journal-title":"arXiv 2001 09614"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/36\/10006360\/10128858.pdf?arnumber=10128858","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T18:46:01Z","timestamp":1765997161000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10128858\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":63,"URL":"https:\/\/doi.org\/10.1109\/tgrs.2023.3277077","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"value":"0196-2892","type":"print"},{"value":"1558-0644","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}