结合未知类特征生成与分类得分修正的SAR目标开集识别方法  

An Open Set Recognition Method for SAR Targets Combining Unknown Feature Generation and Classification Score Modification

作　　者：陈健[1] 雍奇锋 杜兰[1] 尹林伟 CHEN Jian;YONG Qifeng;DU Lan;YIN Linwei(National Key Laboratory of Radar Signal Processing,Xidian University,Xi’an 710071,China)

机构地区：[1]西安电子科技大学雷达信号处理全国重点实验室,西安710071

出　　处：《电子与信息学报》2024年第10期3890-3907,共18页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(U21B2039,62201433);中央高校基本科研业务费专项资金(QTZX23067)。

摘　　要：现有合成孔径雷达(SAR)目标识别方法大多局限于闭集假定,即认为训练模板库内训练目标类别包含全部待测目标类别,不适用于库内已知类和库外未知新类目标共存的真实开放识别环境。针对训练模板库目标类别非完备情况下的SAR目标识别问题,该文提出一种结合未知类特征生成与分类得分修正的SAR目标开集识别方法。该方法在利用已知类学习原型网络保证已知类识别精度的基础上结合对潜在未知类特征分布的先验认知,生成未知类特征更新网络,进一步保证特征空间中已知类、未知类特征的鉴别性。原型网络更新完成后,所提方法挑选各已知类边界特征,并计算边界特征到各自类原型的距离(极大距离),通过极值理论对各已知类极大距离进行概率拟合确定了各已知类最大分布区域。测试阶段在度量待测样本特征与各已知类原型距离预测闭集分类得分的基础上,计算了各距离在对应已知类极大距离分布上的概率,并修正闭集分类得分,实现了拒判概率的自动确定。基于MSTAR实测数据集的实验结果表明,所提方法能够有效表征真实未知类特征分布并提升网络特征空间已知类与未知类特征的鉴别性,可同时实现对库内已知类目标的准确识别和对库外未知类新目标的准确拒判。The existing Synthetic Aperture Radar(SAR)target recognition methods are mostly limited to the closed-set assumption,which considers that the training target categories in training template library cover all the categories to be tested and is not suitable for the open environment with the presence of both known and unknown classes.To solve the problem of SAR target recognition in the case of incomplete target categories in the training template library,an openset SAR target recognition method that combines unknown feature generation with classification score modification is proposed in this paper.Firstly,a prototype network is exploited to get high recognition accuracy of known classes,and then potential unknown features are generated based on prior knowledge to enhance the discrimination of known and unknown classes.After the prototype network being updated,the boundary features of each known class are selected and the distance of each boundary feature to the corresponding class prototype,i.e.,maximum distance,is calculated,respectively.Subsequently the maximum distribution area for each known class is determined by the probability fitting of maximum distances for each known class by using extreme value theory.In the testing phase,on the basis of predicting closed-set classification scores by measuring the distance between the testing sample features and each known class prototype,the probability of each distance in the distribution of the corresponding known class’s maximum distance is calculated,and the closed-set classification scores are corrected to automatically determine the rejection probability.Experiments on measured MSTAR dataset show that the proposed method can effectively represent the distribution of unknown class features and enhance the discriminability of known and unknown class features in the feature space,thus achieving accurate recognition for both known class targets and unknown class targets.

关 键 词：SAR目标识别 开集识别 未知类特征生成 极值理论 分类得分修正 

分 类 号：TN957[电子电信—信号与信息处理]