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GPU-accelerated registration of hyperspectral images using KAZE features

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Abstract

Image registration is a common task in remote sensing, consisting in aligning different images of the same scene. In the particular case of hyperspectral images, the exploitation not only of the spatial information contained in the image but also of the spectral information helps to improve the registration. An example of registration method exploiting all the information contained in the images is HSI–KAZE, which is based on feature detection and detects keypoints using nonlinear diffusion filtering. The algorithm is oriented toward extreme situations in which the images are very different in terms of scale, rotation and displacement. In this paper, an efficient implementation of the HSI–KAZE algorithm on GPU using CUDA is proposed. A detailed analysis of the implementation as well as a performance comparison to an OpenMP multicore implementation is also presented. The resulting algorithm is suitable for on-board processing of high-resolution images.

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Acknowledgements

This work was supported in part by the Consellería de Educación, Universidade e Formación Profesional [Grant Nos. GRC2014/008, ED431C 2018/19 and ED431G/08] and Ministerio de Economía y Empresa, Government of Spain [grant number TIN2016-76373-P] and by Junta de Castilla y Leon - ERDF (PROPHET Project) [Grant No. VA082P17]. All are cofunded by the European Regional Development Fund (ERDF). The work of Álvaro Ordóñez was also supported by Ministerio de Ciencia, Innovación y Universidades, Government of Spain, under a FPU Grant [Grant Nos. FPU16/03537 and EST18/00602].

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Authors and Affiliations

  1. Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain

    Álvaro Ordóñez & Dora B. Heras

  2. Departamento de Electrónica e Computación, Universidade de Santiago de Compostela, Santiago de Compostela, Spain

    Francisco Argüello

  3. Faculty of Electrical Engineering and Computer Science, TU Berlin, Berlin, Germany

    Begüm Demir

Authors
  1. Álvaro Ordóñez
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  2. Francisco Argüello
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  3. Dora B. Heras
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  4. Begüm Demir
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Correspondence to Álvaro Ordóñez.

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Ordóñez, Á., Argüello, F., Heras, D.B. et al. GPU-accelerated registration of hyperspectral images using KAZE features. J Supercomput 76, 9478–9492 (2020). https://doi.org/10.1007/s11227-020-03214-0

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  • DOI: https://doi.org/10.1007/s11227-020-03214-0

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