Accelerating SAR Image Registration Using Swarm-Intelligent GPU Parallelization

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Bingnan Wang¹  Lixiang Ma²  Fan Zhang³  Yingbing Liu⁴  Yingcheng Zhou⁴  Yongsheng Zhou⁴ 

[1] Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China;Beijing Institute of Spacecraft System Engineering, Beijing, China;College of Information Science, and Technology and Interdisciplinary Research Center for Artificial Intelligence, Beijing University of Chemical Technology, Beijing, China;College of Information Science, and Technology, Beijing University of Chemical Technology, Beijing, China;

关键词: Graphics processing unit (GPU);    image registration;    particle swarm optimization (PSO);    synthetic aperture radar (SAR);   

DOI  :  10.1109/JSTARS.2020.3024899

来源: DOAJ

【 摘 要 】

Image registration is an important processing step in synthetic aperture radar (SAR) image applications, such as change detection, and elevation extraction. The cross-correlation method is widely employed to find the matching points to realize image registration due to its effectiveness, and simplicity. However, the large number of pixel operations, and whole image sliding operations make it a computationally intensive problem, and it is difficult to adapt to the situation of increasing amount, and volume of SAR images. Graphics processing unit (GPU) based high-performance computing methods are usually used because of their high parallelism, and efficiency. However, most of these methods do not maximally optimize the computing process according to the characteristics of GPU architecture nor do they reduce the calculation amount of the registration process. In this article, a swarm-intelligent GPU parallel pixel-level registration is proposed, which takes into account not only the acceleration of the correlation operation but also the reduction of searching times. First, for each correlation operation, the GPU parallelization is systematically optimized, including parallel reduction, bank conflict prevention, and instruction optimization. Second, the particle swarm optimization algorithm is implemented by GPU to efficiently search the matching points based on the cross-correlation coefficients. In the process of calculation, the CPU, and GPU have zero-copy, which realizes the complete parallelization of the registration. The experimental results show that the method can achieve $40\times$ speedup for a product-level SAR image.

【 授权许可】

Unknown