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Multi-GPU Registration of High-Resolution Multispectral Images Using HSI-KAZE in a Cluster System

Feature-based registration methods have been demonstrated to be very effective to register multispectral images with large distortions or transformations despite the higher execution time that they require. In this paper, a first approach to a multi-node, multi-GPU implementation of the Hyper-spectral KAZE (HSI-KAZE) method for co-registering bands and multispectral images is presented. Different multispectral datasets are distributed among the available nodes of a cluster using MPI and exploiting the parallel stream-based capabilities of the GPUs inside each node using CUDA.