dc.contributor.author | Qin, Hao | |
dc.contributor.author | Shen, Qiangqiang | |
dc.contributor.author | Zeng, Haijin | |
dc.contributor.author | Chen, Yongyong | |
dc.contributor.author | Lu, Guangming | |
dc.date.accessioned | 2024-04-22T12:23:00Z | |
dc.date.available | 2023-11-13T17:40:25Z | |
dc.date.available | 2024-04-22T12:23:00Z | |
dc.date.issued | 2023 | |
dc.identifier.issn | 0196-2892 | |
dc.identifier.other | WOS:001087759000012 | |
dc.identifier.uri | https://imec-publications.be/handle/20.500.12860/43145.2 | |
dc.source | WOS | |
dc.title | Generalized Nonconvex Low-Rank Tensor Representation for Hyperspectral Anomaly Detection | |
dc.type | Journal article | |
dc.contributor.imecauthor | Zeng, Haijin | |
dc.contributor.orcidimec | Zeng, Haijin::0000-0003-0398-3316 | |
dc.identifier.doi | 10.1109/TGRS.2023.3321789 | |
dc.source.numberofpages | 12 | |
dc.source.peerreview | yes | |
dc.source.beginpage | Art. 5526612 | |
dc.source.endpage | N/A | |
dc.source.journal | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | |
dc.source.issue | N/A | |
dc.source.volume | 61 | |
imec.availability | Published - imec | |
dc.description.wosFundingText | This work was supported in part by the National Natural Science Foundation of China under Grant 62106063, in part by the Guangdong Natural Science Foundation under Grant 2022A1515010819, in part by the Shenzhen Science and Technology Program under Grant RCBS20210609103708013, and in part by the Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies under Grant 2022B1212010005. | |