Multi-GPU registration of high-resolution multispectral images using HSI-KAZE in a cluster system
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Abstract
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 Hyperspectral 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.
Description
This a post-print of the article “Multi-GPU registration of high-resolution multispectral images using HSI-KAZE in a cluster system” published in the Proceedings of IGARSS 2022 – 2022 IEEE International Geoscience and Remote Sensing Symposium. The published article is available on https://doi.org/ 10.1109/IGARSS46834.2022.9884717
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This work was supported in part by Ministerio de Ciencia e Innovación, Government of Spain [grant number ID2019-104834GB-I00], and Consellería de Cultura, Educación e Universidade [grant numbers ED431C 2018/19, and accreditation 2019-2022 ED431G-2019/04]. This work was also supported by Junta de Castilla y León (PROPHET-2 Project) [grant number VA226P20]. All are co-funded by the European Regional Development
Fund (ERDF).
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