Accelerating persistent scatterer pixel selection for InSAR processing

Abstract

Interferometric Synthetic Aperture Radar (InSAR) is a remote sensing technology used for estimating displacement of the earth's surface. Phase unwrapping is the most important step in InSAR processing and relies on successful selection of points that appear stable across a set of satellite images taken over time. This paper presents a new algorithm for selecting these points, a problem known as persistent scatterer selection. The algorithm computes the temporal coherence on the wrapped phase derivative by subtracting phases of a pixel and one of its nearby neighbours. It does not require model assumptions, yet preserves accuracy. Motivated by the abundance of parallelism the algorithm exposes, we have implemented it for GPUs. Evaluation using real-world data shows that the GPU implementation not only offers widely superior performance but also scales linearly with GPU count and workload size. We compare the GPU implementation against a parallel CPU implementation: A consumer grade GPU offers an 18× speedup over a 16-core Ivy Bridge Xeon System, while four GPUs offer 65× speedup. Roofline analysis shows that, on a single GPU, our implementation achieves 83% of the peak FLOP-rate of the dual-CPU system. Additionally, the GPU-based solution consumes 29× less energy than the CPU-only solution.