Accelerating Persistent Scatterer Pixel Selection for InSAR Processing

作者：Reza Tahsin^*; Zimmer Aaron; Delgado Blasco Jose Manuel; Ghuman Parwant; Aasawat Tanuj Kr; Ripeanu Matei

来源：IEEE Transactions on Parallel and Distributed Systems, 2018, 29(1): 16-30.

DOI：10.1109/TPDS.2017.2706291

• GPU acceleration
• image processing
• time series analysis
• remote sensing
• InSAR

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 over time. 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.

• 出版日期2018-1-1