With the continuous improvement in the resolution of synthetic aperture radar (SAR), there are many problems in the interpretation of high-resolution SAR images, such as a large amount of data and low efficiency of target recognition. In this paper, a novel SAR target recognition method based on a two-dimensional bidirectional principal component cooperative representation projection feature ((2D)²PCA-CRP) is proposed. First, (2D)²PCA is used to project the image into the low-dimensional feature space, and the redundant information in the high-resolution SAR image is filtered while considering the spatial structure. Then, the spatial global separability feature and local structure feature of the target in the high-resolution SAR image are extracted by CRP to form the (2D)²PCA-CRP feature. Finally, based on this feature, the nearest neighbour classifier is used to complete the target recognition experiments on MSTAR data. The experiments of this study are divided into three parts using standard operation condition (SOC) samples, type change samples and radar incidence angle change data. The experimental results show that the proposed feature achieves better target recognition performance in high-resolution SAR images.

随着合成孔径雷达（SAR）分辨率的不断提高，高分辨率SAR图像的解译存在数据量大、目标识别效率低等问题。本文提出了一种基于二维双向主成分协同表示投影特征((2D) ² PCA-CRP)的SAR目标识别方法。首先，使用(2D) ² PCA 将图像投影到低维特征空间中，在考虑空间结构的同时过滤高分辨率SAR图像中的冗余信息。然后，通过CRP提取高分辨率SAR图像中目标的空间全局可分性特征和局部结构特征，形成(2D)² PCA-CRP 功能。最后，基于该特征，使用最近邻分类器完成对MSTAR数据的目标识别实验。本研究的实验分为三个部分，使用标准运行条件（SOC）样本、类型变化样本和雷达入射角变化数据。实验结果表明，该特征在高分辨率 SAR 图像中取得了较好的目标识别性能。