A Large-Batch Orthorectification Generation Method Based on Adaptive GPU Thread Parameters and Parallel Calculation
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 t...
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| Vydáno v: | IEEE journal of selected topics in applied earth observations and remote sensing Ročník 16; s. 4638 - 4648 |
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| Hlavní autoři: | , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Piscataway
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
2023
IEEE |
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| ISSN: | 1939-1404, 2151-1535 |
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| 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. |
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| AbstractList | 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. |
| Author | Zhou, Ruyan Hu, Shangcheng Pan, Haiyan Han, Yanling Tong, Xiaohua Liu, Shijie Wang, Jing Yang, Shuhu Hong, Zhonghua Zhang, Yun |
| Author_xml | – sequence: 1 givenname: Ruyan orcidid: 0000-0003-4044-2340 surname: Zhou fullname: Zhou, Ruyan organization: College of Information Technology, Shanghai Ocean University, Shanghai, China – sequence: 2 givenname: Shangcheng surname: Hu fullname: Hu, Shangcheng organization: College of Information Technology, Shanghai Ocean University, Shanghai, China – sequence: 3 givenname: Zhonghua orcidid: 0000-0003-0045-1066 surname: Hong fullname: Hong, Zhonghua organization: College of Information Technology, Shanghai Ocean University, Shanghai, China – sequence: 4 givenname: Xiaohua orcidid: 0000-0002-1045-3797 surname: Tong fullname: Tong, Xiaohua organization: College of Surveying and Geo-Informatics, Tongji University, Shanghai, China – sequence: 5 givenname: Shijie orcidid: 0000-0002-5941-0763 surname: Liu fullname: Liu, Shijie organization: College of Surveying and Geo-Informatics, Tongji University, Shanghai, China – sequence: 6 givenname: Haiyan orcidid: 0009-0004-5565-3022 surname: Pan fullname: Pan, Haiyan organization: College of Information Technology, Shanghai Ocean University, Shanghai, China – sequence: 7 givenname: Yun surname: Zhang fullname: Zhang, Yun organization: College of Information Technology, Shanghai Ocean University, Shanghai, China – sequence: 8 givenname: Yanling surname: Han fullname: Han, Yanling organization: College of Information Technology, Shanghai Ocean University, Shanghai, China – sequence: 9 givenname: Jing surname: Wang fullname: Wang, Jing organization: College of Information Technology, Shanghai Ocean University, Shanghai, China – sequence: 10 givenname: Shuhu orcidid: 0000-0001-9967-7756 surname: Yang fullname: Yang, Shuhu organization: College of Information Technology, Shanghai Ocean University, Shanghai, China |
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| SubjectTerms | Acceleration Adaptive Central processing units CPUs Geometric accuracy Graphics graphics processing unit (GPU) Graphics processing units large batch Mathematical analysis Methods Optimization orthorectification Parameters thread parameters |
| Title | A Large-Batch Orthorectification Generation Method Based on Adaptive GPU Thread Parameters and Parallel Calculation |
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