ABSTRACT

To handle synthetic aperture radar (SAR) image target recognition problem, a multi-view method is proposed. For the multi-view SAR images be recognized, they are first clustered based on the correlation coefficients and divided into several view sets. Afterwards, for the view set containing two or more images, the multiset canonical correlation analysis (MCCA) is employed to fuse them as a single feature vector. For the view set with a single image, its corresponding feature vector is directly used. Finally, the joint sparse representation is used to characterize and classify the feature vectors from different view sets and determine the target label of the multi-view SAR images. Experiments and analysis on the moving and stationary target acquisition and recognition (MSTAR) dataset show that the proposed method can achieve an average recognition rate of 99.42% for 10 types of targets under the standard operating condition (SOC). Its performance is also better than several reference methods under the extended operating conditions (EOC) including noise interference and target occlusion.

摘要

针对合成孔径雷达（SAR）图像目标识别问题，提出了一种多视图方法。对于要识别的多视点SAR图像，首先根据相关系数对它们进行聚类，并划分为多个视点集。之后，对于包含两个或多个图像的视图集，采用多集典型相关分析（MCCA）将它们融合为单个特征向量。对于单幅图像的视图集，直接使用其对应的特征向量。最后，联合稀疏表示用于对来自不同视图集的特征向量进行表征和分类，并确定多视图SAR图像的目标标签。在动静目标采集与识别（MSTAR）数据集上的实验和分析表明，该方法在标准操作条件（SOC）下对10类目标的平均识别率达到99.42%。在包括噪声干扰和目标遮挡在内的扩展操作条件（EOC）下，其性能也优于几种参考方法。