SAR图像车辆目标多模态联合协同表示分类方法

为提高合成孔径雷达图像车辆目标的识别性能,本文提出一种SAR图像车辆目标多模态联合协同表示分类(Joint Multimode Cooperative Representation Classification,JMCRC)方法.首先采用二维变分模态分解技术将SAR图像分解为分别表征全局信息和边缘信息的多个子模态分量,...

Joint Multimode Cooperative Representation Classification for Vehicle Targets in SAR Imagery

In order to improve the identification performance of vehicle targets in Synthetic Aperture Radar (SAR) images, this paper proposes a Joint Multimode Cooperative Representation Classification based Classification (JMCRC) method for SAR image vehicle targets. Firstly, Two Dimensional Variational Mode Decomposition is used to decompose SAR image into multiple sub-modal components representing global information and edge information respectively, and then extracting the two-dimensional bidirectional principal component analysis ((2D)2PCA) characteristics from each sub-modal; Secondly, the Cooperative Representation Classification was extended to the JMCRC, and the original image and features of each sub-mode were combined for the Classification task. The proposed method is verified on the MSTAR dataset and a real recorded dataset, and the results show that the method proposed in this paper achieves better classification performance under the Standard Operating Condition (SOC), specific model recognition, depression angle variance and ample unbalanced experimental conditions.