Abstract

Profiting from the development of space remote sensing technology, the amount of remote sensing image data obtained by satellite is increasing dramatically; however, how to deal with these data quickly and efficiently has turned out to be a great computational challenge. With the rapid development of general-purpose GPU computing technology, researchers improved remote sensing applications based on GPU, and obtained good speedup. However, the current GPU parallel processes are not well adapted to the remote sensing image processing; furthermore, they have data loading, storage, and I/O problems. To solve these bottlenecks, this paper proposes three corresponding optimization strategies, and their effectiveness is confirmed by further experiments.

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Cite this article

@article{cai-12041,
  author  = {Peng Liu and Tao Yuan and Yan Ma and Lizhe Wang and Dingsheng Liu and Shasha Yue and Joanna Kołodziej},
  title   = {Parallel Processing of Massive Remote Sensing Images in a GPU Architecture},
  journal = {Computing and Informatics},
  volume  = {33},
  number  = {1},
  pages   = {197--217},
  year    = {2014},
  issn    = {1335-9150},
  url     = {https://cai.type.sk/content/2014/1/parallel-processing-of-massive-remote-sensing-images-in-a-gpu-architecture/}
}
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