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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Accelerating Persistent Scatterer Pixel Selection for InSAR Processing
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Accelerating Persistent Scatterer Pixel Selection for InSAR Processing

机译：加速用于InSAR处理的持久散射像素选择

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摘要

Interferometric Synthetic Aperture Radar (InSAR) is a remote sensing technology used for estimating the displacement of an object on the ground or the earth's surface itself. Persistent Scatterer-InSAR (PS-InSAR) is a category of time series algorithms enabling high resolution monitoring. PS-InSAR relies on successful selection of points that appear stable across a set of satellite images taken overtime. This paper presents PtSel, a new algorithm for selecting these points, a problem known as Persistent Scatterer Selection. The key advantage of PtSel over the key existing techniques is that it does not require model assumptions, yet preserves solution accuracy. Motivated by the abundance of parallelism the algorithm exposes, we have implemented it for GPUs. Our evaluation using real-world data shows that the GPU implementation not only offers superior performance but also scales linearly with GPU count and workload size. We compare the GPU implementation and a parallel CPU implementation: a consumer grade GPU offers 18x speedup over a 16-core Ivy Bridge Xeon System, while four GPUs offer 65x speedup. The GPU solution consumes 28x less energy than the CPU-only solution. Additionally, we present a comparison with the most widely used PS-interferometry software package StaMPS, in terms of point selection coverage and precision.
机译：干涉合成孔径雷达（InSAR）是一种遥感技术，用于估计物体在地面或地球表面本身的位移。永久散射体InSAR（PS-InSAR）是一类时间序列算法，可实现高分辨率监视。 PS-InSAR依赖于成功选择的点，这些点在一段时间内拍摄的一组卫星图像中看起来很稳定。本文介绍了PtSel，这是一种选择这些点的新算法，称为持久性散射体选择。与现有的关键技术相比，PtSel的主要优势在于它不需要模型假设，但可以保留解决方案的准确性。由于该算法具有大量的并行性，因此我们已经为GPU实现了它。我们使用实际数据进行的评估表明，GPU实施不仅可以提供卓越的性能，而且可以随GPU数量和工作负载大小线性扩展。我们将GPU实施与并行CPU实施进行了比较：消费级GPU的速度比16核Ivy Bridge Xeon系统高18倍，而四个GPU的速度却高65倍。 GPU解决方案的能耗比仅CPU的解决方案少28倍。此外，在点选择范围和精度方面，我们将与使用最广泛的PS干涉仪软件包StaMPS进行比较。

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