Comparing area–based and feature–based methods for co–registration of multispectral bands on GPU
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| Title: | Comparing area–based and feature–based methods for co–registration of multispectral bands on GPU |
| Author: | Ordóñez Iglesias, Álvaro Blanco Heras, Dora Argüello Pedreira, Francisco Santiago |
| Affiliation: | Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías da Información Universidade de Santiago de Compostela. Departamento de Electrónica e Computación |
| Subject: | Multispectral | Registration | CUDA | GPU | |
| Date of Issue: | 2021 |
| Publisher: | IEEE |
| Citation: | Á. Ordóñez, D. B. Heras and F. Argüello, "Comparing Area-Based and Feature-Based Methods for Co-Registration of Multispectral Bands on GPU," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 1575-1578, doi: 10.1109/IGARSS47720.2021.9554152 |
| Abstract: | Registration is required as a previous step for processing multispectral images. The different bands captured by each sensor for each image, as well as the different images corresponding to the same area, need to be aligned. In this paper, a 2– level registration scheme comparing the results obtained by the hyperspectral Fourier–Mellin (HYFM) and hyperspectral KAZE (HSI–KAZE) registration methods is proposed. It is designed for efficient implementation in a multi-GPU system in which different scenes are registered in parallel on different GPUs |
| Description: | This a post-print of the article “Comparing Area-Based and Feature-Based Methods for CoRegistration of Multispectral Bands on GPU” published in the Proceedings of IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium |
| Publisher version: | https://doi.org/10.1109/IGARSS47720.2021.9554152 |
| URI: | http://hdl.handle.net/10347/27070 |
| DOI: | 10.1109/IGARSS47720.2021.9554152 |
| ISBN: | 978-1-6654-0369-6 |
| Rights: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works |
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Except where otherwise noted, this item's license is described as © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works