A dynamic acceleration method for remote sensing image processing based on CUDA

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 full 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. The effectiveness of the proposed DAA method in accelerating remote sensing algorithm with point operations was verified by experiments in this paper, and the experimental results indicated that the DAA method can obtain better performance than traditional methods.

Automated summary

This paper introduces how to optimize the application of GPU in remote sensing image processing using a dynamic adaptive acceleration method, determining calculation parameters based on GPU hardware parameters and dynamically preprocessing input remote sensing images on host, and executing tasks on GPU, the results show that this method outperforms traditional approaches.