Application of CPU-GPU heterogeneous system in optical remote sensing image processing

Dang Yuanyuan; Wang Xin

doi:10.3788/IRLA20200092

Volume 49 Issue S1

Sep. 2020

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Dang Yuanyuan, Wang Xin. Application of CPU-GPU heterogeneous system in optical remote sensing image processing[J]. Infrared and Laser Engineering, 2020, 49(S1): 20200092. doi: 10.3788/IRLA20200092

Citation:

Dang Yuanyuan, Wang Xin. Application of CPU-GPU heterogeneous system in optical remote sensing image processing[J]. Infrared and Laser Engineering, 2020, 49(S1): 20200092. doi: 10.3788/IRLA20200092

Application of CPU-GPU heterogeneous system in optical remote sensing image processing

doi: 10.3788/IRLA20200092

Department of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China

Received Date: 2020-05-11
Rev Recd Date: 2020-06-21
Publish Date: 2020-09-22

Abstract

In recent years, the application of CPU-GPU heterogeneous system in the field of optical remote sensing image data processing has received wide attention. Firstly, the architecture and development of CPU-GPU heterogeneous system were introduced. Next, the process of optical remote sensing image data processing was introduced. Then, the application of CPU-GPU heterogeneous system in optical remote sensing image preprocessing, follow-up processing data processing was introduced. Finally, the application of CPU-GPU heterogeneous system in optical remote sensing image data processing system was analyzed and summarized. The analysis shows that the CPU-GPU heterogeneous system is feasible and has a wide prospect in the field of optical remote sensing image data processing, but still needs to solve the key problems such as parallelizing design and optimization of the algorithm, the load balance of CPU and GPU, which is of great significance to promote the application of the CPU-GPU heterogeneous system in the optical remote sensing image data processing.
- remote sensing image data processing,
- heterogeneous system,
- parallel processing,
- GPU

References

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Application of CPU-GPU heterogeneous system in optical remote sensing image processing

Department of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China

Received Date: 2020-05-11

Rev Recd Date: 2020-06-21

Publish Date: 2020-09-22

Key words:

Abstract: In recent years, the application of CPU-GPU heterogeneous system in the field of optical remote sensing image data processing has received wide attention. Firstly, the architecture and development of CPU-GPU heterogeneous system were introduced. Next, the process of optical remote sensing image data processing was introduced. Then, the application of CPU-GPU heterogeneous system in optical remote sensing image preprocessing, follow-up processing data processing was introduced. Finally, the application of CPU-GPU heterogeneous system in optical remote sensing image data processing system was analyzed and summarized. The analysis shows that the CPU-GPU heterogeneous system is feasible and has a wide prospect in the field of optical remote sensing image data processing, but still needs to solve the key problems such as parallelizing design and optimization of the algorithm, the load balance of CPU and GPU, which is of great significance to promote the application of the CPU-GPU heterogeneous system in the optical remote sensing image data processing.

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Reference (51)

[1]	Gong Dun. Review on mapping space remote sensor optical system[J]. Chinese Optics, 2015, 8(5):714-724. (in Chinese)巩盾.空间遥感测绘光学系统研究综述[J]. 中国光学, 2015, 8(5):714-724.
[2]	Li Deren, Tong Qingxi, Li Rongxing. Current issues in high-resolution Earth observation technology[J]. Sci China Earth Sci, 2012, 42(6):805-813. (in Chinese)李德仁, 童庆禧, 李荣兴. 高分辨率对地观测的若干前沿科学问题[J]. 中国科学:地球科学, 2012, 42(6):805-813.
[3]	Li Deren, Wang Mi, Shen Xin, et al. From earth observation satellite to earth observation brain[J]. Geomatics and Information Science of Wuhan University, 2017, 42(2):143-149. (in Chinese)李德仁, 王密, 沈欣, 等. 从对地观测卫星到对地观测脑[J]. 武汉大学学报(信息科学版), 2017, 42(2):143-149.
[4]	Stone J, Phillips J, Hardy D. Accelerating molecular modeling applications with graphics processors[J]. Journal of Computational Chemistry, 2007, 28(16):2618-2640.
[5]	Lin Yisong, Yang Xuejun, Tang Tao. An integrated energy optimization approach for CPU-GPU heterogeneous systems based on critical path analysis[J]. Chinese Journal of Computers, 2012, 35(1):123-133. (in Chinese)林一松, 杨学军, 唐滔. 一种基于关键路径分析的CPU-GPU异构系统综合能耗优化方法[J]. 计算机学报, 2012, 35(1):123-133.
[6]	Yang Jingyu. Study on parallel processing technologies of photogrammetry data based on GPU[D]. Zhengzhou:PLA Information Engineering University, 2011. (in Chinese)杨靖宇. 摄影测量数据GPU并行处理若干关键技术研究[D]. 郑州:解放军信息工程大学, 2011.
[7]	Bai Hongtao. Research on high performance parallel algorithms based on GPU[D]. Changchun:Jilin University, 2010. (in Chinese)白洪涛. 基于GPU的高性能并行算法研究[D]. 长春:吉林大学, 2010.
[8]	Chen Dongdong. Research on performance optimization of CPU-GPU heterogeneous platform and its application in real-time signal simulation technologies[D]. Hangzhou:Zhejiang University, 2017. (in Chinese)陈冬冬. CPU-GPU异构平台的性能优化研究及其在实时信号模拟技术中的应用[D]. 杭州:浙江大学, 2017.
[9]	Zhang Fan, Han Shukui, Zhang Liguo. Parallel acceleration of Canny algorithm based on GPU[J]. Chinese Optics, 2017, 10(6):737-743. (in Chinese)张帆, 韩树奎, 张立国. Canny算法的GPU并行加速[J]. 中国光学, 2017, 10(6):737-743.
[10]	Luebke D. Cuda:Scalable parallel programming for high-performance scientific computing[C]//5th IEEE International Symposium on Digital Object Identifier. IEEE, 2008:836-838.
[11]	Harish P, Narayanan P J. Accelerating large graph algorithm on the GPU using CUDA[C]//International Conference on High-Performance Computing. Berlin Heidelberg:Springer, 2007:197-208.
[12]	Du P, Weber R, Luszczek P, et al. From CUDA to Open CL:Towards a performance-portable solution for multi-platform GPU programming[J]. Parallel Computing, 2012, 38(8):391-407.
[13]	Xu Xuegui, Zhang Qing. Efficiency processing parallel re mote sensing imagery using CUDA[J]. Geospatial Information, 2011, 9(6):47-53. (in Chinese)许雪贵, 张清. 基于CUDA的高效并行遥感影像处理[J]. 地理空间信息, 2011, 9(6):47-53.
[14]	Fang Liuyang. Research on CPU/GPU cooperative high performance processing for optical satellite remote sensing data[D]. Wuhan:Wuhan University, 2015. (in Chinese)方留杨. CPU/GPU协同的光学卫星遥感数据高性能处理方法研究[D]. 武汉:武汉大学, 2015.
[15]	Fang Liuyang, Wang Mi, Li Deren. A workload-distribution based CPU/GPU MTF compensation approach for high resolution satellite images[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(6):598-606. (in Chinese)方留杨, 王密, 李德仁. 负载分配的CPU/GPU高分辨率卫星影像调制传递补偿方法[J]. 测绘学报, 2014, 43(6):598-606.
[16]	Fang Liuyang, Wang Mi, Li Deren. A CPU-GPU coprocessing orthographic rectification approach for optical satellite imagery[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(10):668-675. (in Chinese)方留杨, 王密, 李德仁. CPU和GPU协同处理的光学卫星遥感影像正射校正方法[J].测绘学报, 2013, 42(10):668-675.
[17]	Dinguirard M, Slater P N. Calibration of space-multispectral imaging sensors:a review.[J]. Remote Sensing of Environment, 1999, 68(3):194-205.
[18]	Wang J N, Gu X F, Ming T, et al. Classification and gradation rule for remote sensing satellite data products.[J]. Journal of Remote Sensing, 2013, 17(3):566-577.
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Application of CPU-GPU heterogeneous system in optical remote sensing image processing

doi: 10.3788/IRLA20200092

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