{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T15:59:42Z","timestamp":1776441582937,"version":"3.51.2"},"reference-count":50,"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\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFB3903401"],"award-info":[{"award-number":["2022YFB3903401"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42241109"],"award-info":[{"award-number":["42241109"]}],"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":["42271350"],"award-info":[{"award-number":["42271350"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"MIAI at Grenoble Alpes","award":["ANR-19-P3IA-0003"],"award-info":[{"award-number":["ANR-19-P3IA-0003"]}]},{"DOI":"10.13039\/501100001961","name":"AXA Research Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001961","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.2023.3279834","type":"journal-article","created":{"date-parts":[[2023,5,25]],"date-time":"2023-05-25T17:52:11Z","timestamp":1685037131000},"page":"1-12","source":"Crossref","is-referenced-by-count":201,"title":["LRR-Net: An Interpretable Deep Unfolding Network for Hyperspectral Anomaly Detection"],"prefix":"10.1109","volume":"61","author":[{"given":"Chenyu","family":"Li","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Southeast University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7311-9844","authenticated-orcid":false,"given":"Bing","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Southeast University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3212-9584","authenticated-orcid":false,"given":"Danfeng","family":"Hong","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1301-9758","authenticated-orcid":false,"given":"Jing","family":"Yao","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4817-2875","authenticated-orcid":false,"given":"Jocelyn","family":"Chanussot","sequence":"additional","affiliation":[{"name":"INRIA, CNRS, Grenoble INP, GIPSA-Laboratory, Université Grenoble Alpes, Grenoble, France"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3390\/rs14091973"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2013.2238609"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.88"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2872590"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1117\/1.3386069"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1117\/12.773444"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2014.2343955"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2015.2493201"},{"key":"ref19","first-page":"888","article-title":"Hyperspectral anomaly detection via convolutional neural network and autoencoder","volume":"57","author":"yang","year":"2019","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3228497"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2010.2098842"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1117\/12.2015652"},{"key":"ref45","volume":"1","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2859203"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2015.2479299"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00168"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3196826"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2883941"},{"key":"ref43","first-page":"399","article-title":"Learning fast approximations of sparse coding","author":"gregor","year":"2010","journal-title":"Proc 27th Int Conf Int Conf Mach Learn"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2008.915928"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2004.841487"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/29.60107"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2585495"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2021.3105440"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3015157"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/79.974724"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1126\/science.228.4704.1147"},{"key":"ref40","doi-asserted-by":"crossref","DOI":"10.1109\/TGRS.2021.3081177","article-title":"SNMF-Net: Learning a deep alternating neural network for hyperspectral unmixing","volume":"60","author":"xiong","year":"2022","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1561\/2200000016"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS47720.2021.9553145"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.3028931"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2878958"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2021.05.029"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.2975295"},{"key":"ref33","first-page":"8217","article-title":"Reconstructing the anomaly image of hyperspectral data using a recurrent neural network","volume":"57","author":"luo","year":"2019","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"ref32","first-page":"4156","article-title":"Hyperspectral anomaly detection using generative adversarial network","author":"chang","year":"2020","journal-title":"Proc 28th ACM Int Conf Multimedia"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/18.857796"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/79.974730"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.2982490"},{"key":"ref38","first-page":"612","article-title":"Linearized alternating direction method with adaptive penalty for low-rank representation","author":"lin","year":"2011","journal-title":"Proc 25th Adv Neural Inf Process Syst"},{"key":"ref24","article-title":"Decoupled-and-coupled networks: Self-supervised hyperspectral image super-resolution with subpixel fusion","author":"hong","year":"2022","journal-title":"arXiv 2205 03742"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/MDM.2018.00029"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3258061"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2839891"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3057721"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2016.7730344"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.2965961"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2944419"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.08.012"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2018.06.001"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/36\/10006360\/10136197.pdf?arnumber=10136197","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T18:37:53Z","timestamp":1687804673000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10136197\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":50,"URL":"https:\/\/doi.org\/10.1109\/tgrs.2023.3279834","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"value":"0196-2892","type":"print"},{"value":"1558-0644","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}