Synthetic Aperture Radar (SAR) Data Compression Based on Cosine Similarity of Point Clouds

This paper proposes a structure-aware compression technique for efficient compression of high-resolution synthetic aperture radar (SAR)-based point clouds by quantitatively analyzing the directional characteristics of local structures. The proposed method computes the angular difference between the principal eigenvector of each point and those of its neighboring points, selectively removing points with low contribution to directional preservation and retaining only structurally significant feature points. The method demonstrates superior information preservation performance through various compression evaluation metrics such as entropy, peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). Additionally, the SHREC’19 human mesh dataset is employed to further assess the generality and robustness of the proposed approach. The results show that the proposed method can maximize data efficiency while preserving the core information of the point cloud through a novel directionality-based structural preservation strategy.