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A GPU-Parallel Image Coregistration Algorithm for InSar Processing at the Edge
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DOI：10.3390/s21175916.png)
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Abstract
En 中文
Image Coregistration for InSAR processing is a time-consuming procedure that is usually processed in batch mode. With the availability of low-energy GPU accelerators, processing at the edge is now a promising perspective. Starting from the individuation of the most computationally intensive kernels from existing algorithms, we decomposed the cross-correlation problem from a multilevel point of view, intending to design and implement an efficient GPU-parallel algorithm for multiple settings, including the edge computing one. We analyzed the accuracy and performance of the proposed algorithm-also considering power efficiency-and its applicability to the identified settings. Results show that a significant speedup of InSAR processing is possible by exploiting GPU computing in different scenarios with no loss of accuracy, also enabling onboard processing using SoC hardware.
Keywords:
InSAR
remote sensing
onboard processing
cross-correlation
GPU-parallel
computation offloading
edge computing
CUDA
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