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首页> 外文期刊>Computers & geosciences >GPU accelerated interferometric SAR processing for Sentinel-1 TOPS data
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GPU accelerated interferometric SAR processing for Sentinel-1 TOPS data

机译：用于Sentinel-1 TOPS数据的GPU加速干涉SAR处理

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摘要

Sentinel-1 (S-1) TOPS data are widely applied in InSAR applications to monitor earthquakes and landslides. The large S-1 coverage however, leads to a high computational cost when executing InSAR techniques. Thus, we develop a GPU accelerated S-1 InSAR processing method implemented on a personal desktop. In the proposed method, computationally expensive modules including geometric coregistration, resampling, Enhanced Spectral Diversity, and coherence estimation, are implemented using CUDA. In addition, several optimizations are employed to enhance the efficiency of these modules. We select an efficient approximation method in the geometric coregistration module, and improve the GPU memory access efficiency in the resampling module through the GPU texture memory, temporary register array, and a configuration with more Ll cache. We develop a novel GPU-based parallel coherence estimation algorithm in ESD and coherence estimation modules, and use the asynchronous data transfer technology to hide the costs of CPU-GPU data transfer for resampling, ESD, and coherence estimation modules. After several optimizations, our GPU-accelerated modules (considering CPU-GPU transmission costs) achieves speedup ratios up to 157x, 166x, 145x, and 168x with respect to their single-threaded CPU counterparts. For a full frame S-1 image, our method reduces the computation time from 1415.32s to 8.59s. Moreover, our method is also validated in two case studies of the 2016 Mw6.2 Central Italy earthquake and 2018 Mw6.9 Leilani Estates earthquake caused by the Kilauea eruption in Hawaii.
机译：Sentinel-1（S-1）TOPS数据已广泛应用于InSAR应用中，以监测地震和滑坡。但是，执行InSAR技术时，较大的S-1覆盖范围会导致较高的计算成本。因此，我们开发了在个人桌面上实现的GPU加速S-1 InSAR处理方法。在所提出的方法中，使用CUDA实现了计算量大的模块，包括几何配准，重采样，增强频谱分集和相干估计。另外，采用了几种优化措施来提高这些模块的效率。我们在几何核心模块中选择一种有效的近似方法，并通过GPU纹理内存，临时寄存器阵列以及具有更多Ll缓存的配置来提高重采样模块中的GPU内存访问效率。我们在ESD和相干性估计模块中开发了一种新颖的基于GPU的并行相干性估计算法，并使用异步数据传输技术来隐藏CPU-GPU数据传输的重采样，ESD和相干性估计模块的成本。经过多次优化后，我们的GPU加速模块（考虑了CPU-GPU的传输成本）相对于单线程CPU同类产品达到了157x，166x，145x和168x的加速比。对于全帧S-1图像，我们的方法将计算时间从1415.32s减少到8.59s。此外，我们的方法在2016年意大利中部Mw6.2地震和2018年夏威夷基拉韦厄火山爆发引起的2018年Mw6.9 Leilani Estates地震的两个案例研究中也得到了验证。

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  • 来源
    《Computers & geosciences》 |2019年第8期|12-25|共14页
  • 作者

    Yu Yanghai; Balz Timo; Luo Heng; Liao Mingsheng; Zhang Lu;

  • 作者单位

    Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China;

    Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China;

    Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China;

    Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China;

    Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    InSAR; Sentinel-1; TOPS; GPU; CUDA;

    机译：InSAR;Sentinel-1;TOPS;GPU;CUDA;

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