Threshold Filtering Semi-Supervised Learning Method for SAR Target Recognition

作     者：Linshan Shen Ye Tian Liguo Zhang Guisheng Yin Tong Shuai Shuo Liang Zhuofei Wu 

作者机构：Harbin Engineering UniversityCollege of Computer Science and TechnologyHarbin150001China Xidian UniversityXi’an710000China The 54th Research Institute of CETCShijiazhuang050000China University of BolognaBologna40100Italy 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第73卷第10期

页      面：465-476页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Our research is funded by National Key R&D Program of China(2021YFC3320302) Fundamental Research(JCKY2020210B019) Natural Science Foundation of Heilongjiang Province(No.F2018006) Network threat depth analysis software(KY10800210013). 

主　　题：Semi-supervised learning SAR target recognition threshold filtering out-of-class data 

摘      要：The semi-supervised deep learning technology driven by a small part of labeled data and a large amount of unlabeled data has achieved excellent performance in the field of image processing.However,the existing semisupervised learning techniques are all carried out under the assumption that the labeled data and the unlabeled data are in the same distribution,and its performance is mainly due to the two being in the same distribution state.When there is out-of-class data in unlabeled data,its performance will be affected.In practical applications,it is difficult to ensure that unlabeled data does not contain out-of-category data,especially in the field of Synthetic Aperture Radar(SAR)image recognition.In order to solve the problem that the unlabeled data contains out-of-class data which affects the performance of the model,this paper proposes a semi-supervised learning method of threshold filtering.In the training process,through the two selections of data by the model,unlabeled data outside the category is filtered out to optimize the performance of the model.Experiments were conducted on the Moving and Stationary Target Acquisition and Recognition(MSTAR)dataset,and compared with existing several state-of-the-art semi-supervised classification approaches,the superiority of our method was confirmed,especially when the unlabeled data contained a large amount of out-of-category data.