{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T15:20:01Z","timestamp":1773415201865,"version":"3.50.1"},"reference-count":82,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":["61801353"],"award-info":[{"award-number":["61801353"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Foundation of Guangdong Testing Institute of Product Quality Supervision","award":["2025zz01"],"award-info":[{"award-number":["2025zz01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Geosci. Remote Sensing"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tgrs.2025.3561174","type":"journal-article","created":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T13:03:02Z","timestamp":1746536582000},"page":"1-15","source":"Crossref","is-referenced-by-count":5,"title":["CL-BioGAN: Biologically Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection"],"prefix":"10.1109","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6704-1198","authenticated-orcid":false,"given":"Jianing","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, School of Computer Science and Technology, Xidian University, Xi’an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5145-3253","authenticated-orcid":false,"given":"Zheng","family":"Hua","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education of China, School of Artificial Intelligence, Xidian University, Xi’an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5029-8492","authenticated-orcid":false,"given":"Wan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, School of Computer Science and Technology, Xidian University, Xi’an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengjia","family":"Hao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education of China, School of Artificial Intelligence, Xidian University, Xi’an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqiong","family":"Yao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education of China, School of Artificial Intelligence, Xidian University, Xi’an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0415-8556","authenticated-orcid":false,"given":"Maoguo","family":"Gong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi’an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2024.3397086"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2021.3091156"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2024.3392189"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2024.3386578"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.01.012"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3057446"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3213473"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/s0079-7421(08)60536-8"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-07711-7"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3367329"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2024.3376197"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-023-00747-w"},{"key":"ref13","first-page":"22379","article-title":"AFEC: Active forgetting of\n                        negative transfer in continual learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1038\/s41593-021-00809-5"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.69884"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.tics.2020.09.004"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00282"},{"key":"ref19","article-title":"Integrating present and past in unsupervised\n                        continual learning","author":"Zhang","year":"2024","journal-title":"arXiv:2404.19132"},{"key":"ref20","article-title":"Learning prompt with\n                        distribution-based feature replay for few-shot class-incremental\n                        learning","author":"Huang","year":"2024","journal-title":"arXiv:2401.01598"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICSP56322.2022.9965230"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00525"},{"key":"ref23","article-title":"BiRT: Bio-inspired replay in vision transformers for\n                        continual learning","author":"Jeeveswaran","year":"2023","journal-title":"arXiv:2305.04769"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2024.3355879"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2024.3397048"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093365"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01636"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS46834.2022.9884527"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2023.3280029"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01148"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01225-0_5"},{"key":"ref32","first-page":"3925","article-title":"Learn to grow: A continual structure learning\n                        framework for overcoming catastrophic forgetting","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00025"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58621-8_23"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2022.3226250"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00025"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19809-0_13"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20053-3_10"},{"key":"ref39","first-page":"14150","article-title":"Optimizing reusable knowledge for continual learning\n                        via metalearning","volume-title":"Proc. Adv. Neural Inf.\n                        Process. Syst.","author":"Hurtado"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00019"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19809-0_40"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00940"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00024"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01146"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19809-0_36"},{"key":"ref46","first-page":"5682","article-title":"S-prompts learning with pre-trained transformers: An\n                        occam\u2019s Razor for domain incremental\n                    learning","volume-title":"Proc. Adv. Neural Inf. Process.\n                        Syst.","volume":"35","author":"Wang"},{"key":"ref47","article-title":"Progressive prompts: Continual learning for language\n                        models","author":"Razdaibiedina","year":"2023","journal-title":"arXiv:2301.12314"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2017.2710618"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2004.841487"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2013.2257670"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.3390\/rs14030797"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.3390\/rs15030589"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2023.3249344"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2014.2343955"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2015.2493201"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2022.3161632"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2023.3249748"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2022.3175771"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2021.3128183"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00054"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3267890"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2023.3334562"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3057721"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.23919\/Eusipco47968.2020.9287675"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2020.3044312"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.2975295"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2023.3312589"},{"key":"ref68","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109795","article-title":"Hyperspectral anomaly detection based on variational\n                        background inference and generative adversarial\n                    network","volume":"143","author":"Wang","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref69","article-title":"Variational continual\n                    learning","author":"Nguyen","year":"2017","journal-title":"arXiv:1710.10628"},{"key":"ref70","article-title":"Discrimination among semi-arid landscape endmembers\n                        using the spectral angle mapper (SAM) algorithm","volume-title":"Proc. JPL, Summaries 3rd Annu. JPL Airborne Geosci.\n                        Workshop","volume":"1","author":"Yuhas"},{"key":"ref71","first-page":"3","article-title":"Autoencoders, minimum description length and\n                        Helmholtz free energy","volume-title":"Proc. Adv. Neural\n                        Inf. Process. Syst.","volume":"6","author":"Hinton"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2021.3094884"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3141843"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.2307\/2984875"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2710145"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2023.3258067"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_9"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0080-x"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2024.3388426"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1093\/clinchem\/39.4.561"},{"key":"ref81","first-page":"657","article-title":"A coherent interpretation of AUC as a measure of\n                        aggregated classification performance","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Ferri"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2021.3117765"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/36\/10807682\/10988607.pdf?arnumber=10988607","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T00:56:13Z","timestamp":1755910573000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10988607\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":82,"URL":"https:\/\/doi.org\/10.1109\/tgrs.2025.3561174","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"value":"0196-2892","type":"print"},{"value":"1558-0644","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}