【作者单位】1School of Electrical and Information Engineering, Tianjin University, Tianjin, China 2China Electric Power Research Institute, State Grid Corporation of China, Beijing, China 3Health Science Center, School of Biomedical Engineering, Shenzhen University, Shenzhen, China 4School of Control Science and Engineering, Shandong University, Jinan, China 5Department of Electrical Engineering, Tibet University, Lhasa, China 6Department of Electrical Engineering, Wright State University, Dayton, OH, USA
【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 semisupervised methods to construct learning models, wh...
