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.

中文翻译：

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使用 Swarm-Intelligent GPU 并行化加速 SAR 图像配准

图像配准是合成孔径雷达 (SAR) 图像应用中的重要处理步骤，例如变化检测和高程提取。互相关方法由于其有效性和简单性而被广泛用于寻找匹配点以实现图像配准。然而，大量的像素操作和整幅图像的滑动操作使其成为一个计算密集型问题，难以适应SAR图像数量和体积不断增加的情况。通常使用基于图形处理单元 (GPU) 的高性能计算方法，因为它们具有高并行性和效率。然而，这些方法大多没有根据GPU架构的特点最大限度地优化计算过程，也没有减少配准过程的计算量。在本文中，提出了一种群智能GPU并行像素级配准，它不仅考虑了相关运算的加速，还考虑了搜索次数的减少。首先，针对每一个相关操作，系统地优化GPU并行化，包括并行缩减、bank冲突预防和指令优化。其次，通过GPU实现粒子群优化算法，基于互相关系数高效搜索匹配点。在计算过程中，CPU、GPU都有零拷贝，实现了注册的完全并行化。实验结果表明，对于产品级的SAR图像，该方法可以实现$40\times$的加速。