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Erschienen in: The Journal of Supercomputing 12/2020

28.02.2020

GPU-accelerated registration of hyperspectral images using KAZE features

verfasst von: Álvaro Ordóñez, Francisco Argüello, Dora B. Heras, Begüm Demir

Erschienen in: The Journal of Supercomputing | Ausgabe 12/2020

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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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Literatur
1.
Zurück zum Zitat Agrawal M, Konolige K, Blas MR (2008) Censure: center surround extremas for realtime feature detection and matching. In: European Conference on Computer Vision, Springer, pp 102–115 Agrawal M, Konolige K, Blas MR (2008) Censure: center surround extremas for realtime feature detection and matching. In: European Conference on Computer Vision, Springer, pp 102–115
2.
Zurück zum Zitat Alcantarilla PF, Bartoli A, Davison AJ (2012) KAZE features. In: European Conference on Computer Vision, Springer, pp 214–227 Alcantarilla PF, Bartoli A, Davison AJ (2012) KAZE features. In: European Conference on Computer Vision, Springer, pp 214–227
3.
Zurück zum Zitat Alcantarilla PF, Nuevo J, Bartoli A (2011) Fast explicit diffusion for accelerated features in nonlinear scale spaces. IEEE Trans Pattern Anal Mach Intell 34(7):1281–1298 Alcantarilla PF, Nuevo J, Bartoli A (2011) Fast explicit diffusion for accelerated features in nonlinear scale spaces. IEEE Trans Pattern Anal Mach Intell 34(7):1281–1298
5.
Zurück zum Zitat Fan Z, Vetter C, Guetter C, Yu D, Westermann R, Kaufman A, Xu C (2008) Optimized GPU implementation of learning-based non-rigid multi-modal registration. In: Medical imaging. International Society for Optics and Photonics, pp 69142Y–69142Y Fan Z, Vetter C, Guetter C, Yu D, Westermann R, Kaufman A, Xu C (2008) Optimized GPU implementation of learning-based non-rigid multi-modal registration. In: Medical imaging. International Society for Optics and Photonics, pp 69142Y–69142Y
6.
Zurück zum Zitat Garcia V, Debreuve E, Barlaud M (2008) Fast k nearest neighbor search using GPU. In: 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, IEEE, pp 1–6 Garcia V, Debreuve E, Barlaud M (2008) Fast k nearest neighbor search using GPU. In: 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, IEEE, pp 1–6
7.
Zurück zum Zitat Hasan M, Jia X, Robles-Kelly A, Zhou J, Pickering MR (2010) Multi-spectral remote sensing image registration via spatial relationship analysis on SIFT keypoints. In: Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International, IEEE, pp 1011–1014 Hasan M, Jia X, Robles-Kelly A, Zhou J, Pickering MR (2010) Multi-spectral remote sensing image registration via spatial relationship analysis on SIFT keypoints. In: Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International, IEEE, pp 1011–1014
8.
Zurück zum Zitat Li Q, Wang G, Liu J, Chen S (2009) Robust scale-invariant feature matching for remote sensing image registration. IEEE Geosci Remote Sens Lett 6(2):287–291CrossRef Li Q, Wang G, Liu J, Chen S (2009) Robust scale-invariant feature matching for remote sensing image registration. IEEE Geosci Remote Sens Lett 6(2):287–291CrossRef
9.
Zurück zum Zitat Lowe A, Harrison N, French AP (2017) Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress. Plant Methods 13(1):80CrossRef Lowe A, Harrison N, French AP (2017) Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress. Plant Methods 13(1):80CrossRef
10.
Zurück zum Zitat Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110CrossRef Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110CrossRef
11.
Zurück zum Zitat Mehmet F, Yardimci Y, Temlzel A et al (2009) Registration of multispectral satellite images with orientation-restricted SIFT. In: Geoscience and Remote Sensing Symposium, 2009 IEEE International, IGARSS 2009, vol. 3. IEEE, pp III–243 Mehmet F, Yardimci Y, Temlzel A et al (2009) Registration of multispectral satellite images with orientation-restricted SIFT. In: Geoscience and Remote Sensing Symposium, 2009 IEEE International, IGARSS 2009, vol. 3. IEEE, pp III–243
12.
Zurück zum Zitat Munir M, Wilson DI, Yu W, Young B (2018) An evaluation of hyperspectral imaging for characterising milk powders. J Food Eng 221:1–10CrossRef Munir M, Wilson DI, Yu W, Young B (2018) An evaluation of hyperspectral imaging for characterising milk powders. J Food Eng 221:1–10CrossRef
17.
Zurück zum Zitat Ordóñez Á, Argüello F, Heras BD (2017) GPU accelerated FFT-based registration of hyperspectral scenes. IEEE J Sel Top Appl Earth Obs Remote Sens 10(11):4869–4878CrossRef Ordóñez Á, Argüello F, Heras BD (2017) GPU accelerated FFT-based registration of hyperspectral scenes. IEEE J Sel Top Appl Earth Obs Remote Sens 10(11):4869–4878CrossRef
18.
Zurück zum Zitat Ramkumar B, Laber R, Bojinov H, Hegde RS (2019) GPU acceleration of the KAZE image feature extraction algorithm. J Real Time Image Process 11:1–4 Ramkumar B, Laber R, Bojinov H, Hegde RS (2019) GPU acceleration of the KAZE image feature extraction algorithm. J Real Time Image Process 11:1–4
19.
Zurück zum Zitat Sah S, Vanek J, Roh Y, Wasnik R (2012) GPU accelerated real time rotation, scale and translation invariant image registration method. In: Campilho A, Kamel M (eds) Image analysis and recognition. Springer, Berlin, Heidelberg, pp 224–233CrossRef Sah S, Vanek J, Roh Y, Wasnik R (2012) GPU accelerated real time rotation, scale and translation invariant image registration method. In: Campilho A, Kamel M (eds) Image analysis and recognition. Springer, Berlin, Heidelberg, pp 224–233CrossRef
20.
Zurück zum Zitat Sanders J, Kandrot E (2010) CUDA by example: an introduction to general-purpose GPU programming. Addison-Wesley Professional, Boston Sanders J, Kandrot E (2010) CUDA by example: an introduction to general-purpose GPU programming. Addison-Wesley Professional, Boston
21.
Zurück zum Zitat Shams R, Sadeghi P, Kennedy R, Hartley R (2010) Parallel computation of mutual information on the GPU with application to real-time registration of 3D medical images. Comput Methods Programs Biomed 99(2):133–146CrossRef Shams R, Sadeghi P, Kennedy R, Hartley R (2010) Parallel computation of mutual information on the GPU with application to real-time registration of 3D medical images. Comput Methods Programs Biomed 99(2):133–146CrossRef
22.
Zurück zum Zitat Vince R, More SS (2018) Hyperspectral imaging for detection of Parkinson’s disease. US Patent App. 10/098,540 Vince R, More SS (2018) Hyperspectral imaging for detection of Parkinson’s disease. US Patent App. 10/098,540
23.
Zurück zum Zitat Yi Z, Zhiguo C, Yang X (2008) Multi-spectral remote image registration based on SIFT. Electron Lett 44(2):107–108CrossRef Yi Z, Zhiguo C, Yang X (2008) Multi-spectral remote image registration based on SIFT. Electron Lett 44(2):107–108CrossRef
24.
Zurück zum Zitat Zhang Y, Zhou P, Ren Y, Zou Z (2014) GPU-accelerated large-size VHR images registration via coarse-to-fine matching. Comput Geosci 66:54–65CrossRef Zhang Y, Zhou P, Ren Y, Zou Z (2014) GPU-accelerated large-size VHR images registration via coarse-to-fine matching. Comput Geosci 66:54–65CrossRef
25.
Zurück zum Zitat Zitova B, Flusser J (2003) Image registration methods: a survey. Image Vis Comput 21(11):977–1000CrossRef Zitova B, Flusser J (2003) Image registration methods: a survey. Image Vis Comput 21(11):977–1000CrossRef
Metadaten
Titel
GPU-accelerated registration of hyperspectral images using KAZE features
verfasst von
Álvaro Ordóñez
Francisco Argüello
Dora B. Heras
Begüm Demir
Publikationsdatum
28.02.2020
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 12/2020
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-020-03214-0

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