.png){kind=icon}
.png){kind=icon}
Return
Accelerating Persistent Scatterer Pixel Selection for InSAR Processing
.png){kind=icon}
PRE
AI
DOI：10.1109/TPDS.2017.2706291.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
GPU acceleration
image processing
time series analysis
remote sensing
InSAR
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6
Papers:
5.2K
Citations:
1.1W