【作者单位】1School of Electrical and Information Engineering, Tianjin University, Tianjin, China;2Health Science Center, School of Biomedical Engineering, Shenzhen University, Shenzhen, China;3School of Control Science and Engineering, Shandong University, Jinan, China;4Department of Electrical Engineering, Tibet University, Lhasa, China
【年份】2023
【页码】1
【ISSN】0196-2892
【摘要】 Hyperspectral anomaly detection is of great value in both practical and theoretical terms. However, due to the lack of available semantic labels, previous works mainly relied on unsupervised or semi-supervised methods to construct learning models, w...
