Accelerating SAR Image Registration Using Swarm-Intelligent GPU Parallelization

Abstract

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. 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 paper, 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 (PSO) 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× speedup for a product-level SAR image.

Multireceiver SAS Imagery Based on Monostatic Conversion

“…For a particular receiver's data, the differential preprocessing is also independently conducted. Therefore, both operations can be optimized by parallel algorithms like graphics process unit (GPU) [33] and fastest Fourier transform in the west (FFTW) [34].…”

ATHENA: a GPU-based Framework for Biomedical 3D Rigid Image Registration

“…Software frameworks rely entirely on software libraries, while accelerated frameworks use hardware acceleration to achieve higher performance. On this path, GPUs have proven effective in accelerating image registration procedures [13], [15]- [18].…”

Multi-Class Double-Transformation Network for SAR Image Registration

“…SAR image registration [10][11][12] is used to match two SAR images by exploring the geometric transformation model between them, where two images are called the reference image and the sensed image. At present, many registration methods [13][14][15][16][17] have been proposed to achieve the registration of two SAR images, and they can be simply divided into the traditional registration methods [13,18,19] and the deep-learning-based registration methods [12,[20][21][22][23]. Due to the prominent performance of deep learning, the deep-learningbased method of SAR image registration has recently received more attention compared to traditional methods.…”