A Large-Batch Orthorectification Generation Method Based on Adaptive GPU Thread Parameters and Parallel Calculation

Abstract

Orthorectification reflects a large amount of real and objective information, such as the characteristics of images and the geometric accuracy of maps. Conducting a large batch of orthorectification is a process with high time cost owing to the pixelwise correction each image. A common approach is to use graphics processing unit (GPU) parallel computing to accelerate orthorectification processing. However, most of the existing GPU acceleration studies have adopted experimental testing methods to determine thread parameters, which are inapplicable to different GPUs and affect the GPU acceleration performance. We put forward an adaptive calculation method for GPU thread parameters based on the performance parameters of different GPUs and by simultaneously blocking the image automatically according to the GPU memory space. We used 112 ZY-3 images to test the adaptive GPU and compare it to a general GPU. The experimental results show the following: first, for a single ZY-3 image, the GPU acceleration by the adaptive calculation method presented in this article is 43.22% higher than that by the general GPU, and the correction time is 34.41 times faster than that of the central processing unit. The result of the automatic image blocking was the same as that of the artificial blocking. Second, the experimental performance on four different GPUs indicated that all GPUs exhibited a significant speed boost. Third, for large-batch images, the GPU acceleration by the adaptive GPU was 32.6% higher than that by the general GPU, which provides an adaptive optimization strategy for large-batch image orthorectification.