Two-dimensional bidirectional principal component collaborative projection feature for SAR vehicle target recognition

Abstract

Abstract 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.

Keywords

• Synthetic aperture radar
• Target recognition
• Feature extraction
• Principal component analysis
• Collaborative projection