Overview of Individual Identification Methods for Radiation Sources Under Harsh Data Conditions
Individual identification methods for radiation sources under harsh data conditions are analyzed and compared.Individual recognition methods including imbalance,mislabeling,small samples,and weak labeling are summarized,the advantages and limitations of radiation source feature extraction methods are explored,the key and difficult feature extrac-tion methods in the methods are summarized,and the advantages of deep learning in deep feature extraction and its broad application prospects in the field of radiation source individual recognition are pointed out,with a view providing a com-prehensive supplement to individual identification methods for radiation sources in various situations.
individual identification for radiation sourcesimbalance identificationsmall sample identificationmislabe-ingweak labelingdeep learning
国家科技期刊平台
NSTL
万方数据
