Research Article
A Dynamic Acceleration Method for Remote Sensing Image Processing Based on CUDA
@INPROCEEDINGS{10.1007/978-3-030-72792-5_34, author={Xianyu Zuo and Zhe Zhang and Baojun Qiao and Junfeng Tian and Liming Zhou and Yunzhou Zhang}, title={A Dynamic Acceleration Method for Remote Sensing Image Processing Based on CUDA}, proceedings={Simulation Tools and Techniques. 12th EAI International Conference, SIMUtools 2020, Guiyang, China, August 28-29, 2020, Proceedings, Part I}, proceedings_a={SIMUTOOLS}, year={2021}, month={4}, keywords={Remote sensing data Image processing CUDA stream Dynamic acceleration}, doi={10.1007/978-3-030-72792-5_34} }- Xianyu Zuo
Zhe Zhang
Baojun Qiao
Junfeng Tian
Liming Zhou
Yunzhou Zhang
Year: 2021
A Dynamic Acceleration Method for Remote Sensing Image Processing Based on CUDA
SIMUTOOLS
Springer
DOI: 10.1007/978-3-030-72792-5_34
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.
