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A Dynamic Acceleration Method for Remote Sensing Image Processing Based on CUDA

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Simulation Tools and Techniques (SIMUtools 2020)

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Abstract

The incredible increase in the volume of remote sensing data has made the concept of Remote Sensing as Big Data reality with recent technological developments. Remote sensing image processing is characterized with features of massive data processing and intensive computation, which makes the processes difficult. To optimize the remote sensing image processing for GPU, compute unified device architecture (CUDA) is widely used to implement remote sensing algorithms. However, the usage of GPU in remote sensing image processing has been constrained by the complexity of its implementation and configuration. Therefore, how to take fully advantage of the parallel organization of GPU architecture is awfully challenging. In this paper, a dynamic adaptive acceleration (DAA) method is proposed to determine calculation parameters of GPU adaptively and preprocess the input remote sensing images on host dynamically. By this method, we determine calculation parameters according to the hardware parameters of GPU firstly. And then, the input remote sensing images are reconstructed based on the calculation parameters. Finally, the preprocessed image blocks are arranged to stream tasks and executed on GPU respectively. Effectiveness of the proposed DAA method in accelerate remote sensing algorithm with point operations were verified by experiments in this paper, and the experimental results indicated that the DAA method can obtain better performance than traditional methods.

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Acknowledgements

The authors would like to thank the referees and Editor for their helpful suggestions for revising this manuscript. The project is supported in partly by National Key Research and Development Program of China (2017YFD0301105), Natural Science Foundation of China (61202098, U1604145, U1704122), Science and Technological Research of Key Projects of Henan Province (202102110121, 202102210352, 202102210368, 192102210096, 201400210300), and Excellent Youth Foundation of Science Technology Innovation of Henan Province (184100510004).

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Authors and Affiliations

  1. Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, 475004, People’s Republic of China

    Xianyu Zuo, Zhe Zhang & Baojun Qiao

  2. Henan Engineering Laboratory of Spatial Information Processing, Henan University, Kaifeng, 475004, People’s Republic of China

    Baojun Qiao, Junfeng Tian & Liming Zhou

  3. College of Computer and Information Engineering, Henan University, Kaifeng, 475004, China

    Xianyu Zuo, Zhe Zhang, Junfeng Tian & Liming Zhou

  4. National Cultural Heritage Administration, Beijing, 100010, People’s Republic of China

    Yunzhou Zhang

Authors
  1. Xianyu Zuo
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  2. Zhe Zhang
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  3. Baojun Qiao
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  4. Junfeng Tian
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  5. Liming Zhou
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  6. Yunzhou Zhang
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Editor information

Editors and Affiliations

  1. Embry-Riddle Aeronautical University, Daytona Beach, FL, USA

    Houbing Song

  2. School of Astronautics and Aeronautic, UESTC, Chengdu, China

    Dingde Jiang

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Zuo, X., Zhang, Z., Qiao, B., Tian, J., Zhou, L., Zhang, Y. (2021). A Dynamic Acceleration Method for Remote Sensing Image Processing Based on CUDA. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 369. Springer, Cham. https://doi.org/10.1007/978-3-030-72792-5_34

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  • DOI: https://doi.org/10.1007/978-3-030-72792-5_34

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-72792-5

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