Complex Image Registration Algorithm for Interferometric Synthetic Aperture Sonar in GPU Environment

Zhong, Heping; Tang, Jinsong; Wu, Haoran; Ma, Mengbo

doi:10.1109/cisp-bmei.2018.8633174

Cited by 1 publication

(2 citation statements)

References 9 publications

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“…In [26], merge visits are used to avoid bank conflict problems. In [28], shared memory is used to reasonably optimize the registration process, and OpenMP is used to optimize some tasks on the CPU side. However, the use of these optimization methods is often not comprehensive enough, and the special architecture of the GPU is not systematically considered.…”

Section: Introductionmentioning

confidence: 99%

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Accelerating SAR Image Registration Using Swarm-Intelligent GPU Parallelization

Liu

Zhou

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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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.

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Section: Introductionmentioning

confidence: 99%

“…The parallel SAR image registration [28] based on multilevel GPU optimization offers obvious performance advantages over serial algorithms. However, the sliding process of the two images is still time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Accelerating SAR Image Registration Using Swarm-Intelligent GPU Parallelization

Liu

Zhou

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

Complex Image Registration Algorithm for Interferometric Synthetic Aperture Sonar in GPU Environment

Cited by 1 publication

References 9 publications

Accelerating SAR Image Registration Using Swarm-Intelligent GPU Parallelization

Accelerating SAR Image Registration Using Swarm-Intelligent GPU Parallelization

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