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Extended attribute profiles on GPU applied to hyperspectral image classification

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Abstract

Extended profiles are an important technique for modelling the spatial information of hyperspectral images at different levels of detail. They are used extensively as a preprocessing stage, especially in classification schemes. In particular, attribute profiles, based on the application of morphological attribute filters to the connected components of the image, have been shown to provide very good results. In this paper we present a parallel implementation of the attribute profiles in CUDA for multispectral and hyperspectral imagery considering the attributes area and standard deviation. The profile computation is based on the max-tree approach but without building the tree itself. Instead, a matrix-based data structure is used along with a recursive flooding (component merging) and filter process. Additionally, a previous feature extraction stage based on wavelets is applied to the hyperspectral image in order to extract the most valuable spectral information, reducing the size of the resulting profile. This scheme efficiently exploits the thousands of available threads on the GPU, obtaining a considerable reduction in execution time as compared to the OpenMP CPU implementation.

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Notes

  1. http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes.

  2. https://earth.esa.int/web/guest/-/worldview-2-european-cities-data set.

References

  1. Breen EJ, Jones R (1996) Attribute openings, thinnings, and granulometries. Comput Vis Image Underst 64(3):377–389

    Article  Google Scholar 

  2. Carlinet E, Géraud T (2013) A comparison of many max-tree computation algorithms. In: Hendriks CLL, Borgefors G, Strand R (eds) Mathematical morphology and its applications to signal and image processing. Springer, Berlin, pp 73–85

    Chapter  Google Scholar 

  3. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:27:1–27:27

    Google Scholar 

  4. Darbon J, Akgül CB (2005) An efficient algorithm for attribute openings and closings. In: 2005 13th European Signal Processing Conference, pp 1–4

  5. Ghamisi P, Mura MD, Benediktsson JA (2015) A survey on spectral–spatial classification techniques based on attribute profiles. IEEE Trans Geosci Remote Sens 53(5):2335–2353

    Article  Google Scholar 

  6. Ghamisi P, Maggiori E, Li S, Souza R, Tarabalka Y, Moser G, De Giorgi A, Fang L, Chen Y, Chi M, Serpico S, Benediktsson J (2018) New frontiers in spectral–spatial hyperspectral image classification: the latest advances based on mathematical morphology, markov random fields, segmentation, sparse representation, and deep learning. IEEE Geosci Remote Sens Mag 6(3):10–43

    Article  Google Scholar 

  7. Ma Y, Chen L, Liu P, Lu K (2016) Parallel programing templates for remote sensing image processing on GPU architectures: design and implementation. Computing 98(1):7–33

    Article  MathSciNet  Google Scholar 

  8. Matas P, Dokládalová E, Akil M, Grandpierre T, Najman L, Poupa M, Georgiev V (2008) Parallel algorithm for concurrent computation of connected component tree. Springer, Berlin, pp 230–241

    Google Scholar 

  9. Meijster A, Wilkinson MHF (2002) A comparison of algorithms for connected set openings and closings. IEEE Trans Pattern Anal Mach Intell 24(4):484–494

    Article  Google Scholar 

  10. Moschini U, Meijster A, Wilkinson MHF (2018) A hybrid shared-memory parallel max-tree algorithm for extreme dynamic-range images. IEEE Trans Pattern Anal Mach Intell 40(3):513–526

    Article  Google Scholar 

  11. Mura MD, Benediktsson JA, Waske B, Bruzzone L (2010) Morphological attribute profiles for the analysis of very high resolution images. IEEE Trans Geosci Remote Sens 48(10):3747–3762

    Article  Google Scholar 

  12. Oliveira V, de Alencar Lotufo R (2010) A study on connected components labeling algorithms using GPUs. In: Proceedings: 23rd SIBGRAPI Conference on Graphics, Patterns and Images

  13. Plaza A, Du Q, Chang Y, King RL (2011) High performance computing for hyperspectral remote sensing. IEEE J Sel Top Appl Earth Obs Remote Sens 4(3):528–544

    Article  Google Scholar 

  14. Quesada-Barriuso P, Argüello F, Heras DB, Benediktsson JA (2015) Wavelet-based classification of hyperspectral images using extended morphological profiles on graphics processing units. IEEE J Select Top Appl Earth Obs Remote Sens 8(6):2962–2970

    Article  Google Scholar 

  15. Quesada-Barriuso P, Heras DB, Argüello F, Demir B (2018) GPU computation of attribute profiles for remote sensing image classification. In: Aguiar JV (ed) Proceedings of the 18th International Conference on Computational and Mathematical Methods in Science and Engineering

  16. Salembier P, Oliveras A, Garrido L (1998) Antiextensive connected operators for image and sequence processing. IEEE Trans Image Process 7(4):555–570

    Article  Google Scholar 

  17. Tarjan RE (1975) Efficiency of a good but not linear set union algorithm. J ACM 22(2):215–225

    Article  MathSciNet  MATH  Google Scholar 

  18. Vincent L (1993) Grayscale area openings and closings, their efficient implementation and applications. In: Proceedings of EURASIP Workshop on Mathematical Morphology and its Applications to Signal Processing, pp 22–27

  19. Wilkinson MHF, Roerdink JBTM (2000) Fast morphological attribute operations using Tarjan’s union-find algorithm. Springer, Boston, pp 311–320

    MATH  Google Scholar 

  20. Wilkinson MHF, Gao H, Hesselink WH, Jonker JE, Meijster A (2008) Concurrent computation of attribute filters on shared memory parallel machines. IEEE Trans Pattern Anal Mach Intell 30(10):1800–1813

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the Consellería de Cultura, Educación e Ordenación Universitaria [Grant numbers GRC2014/008 and ED431G/08] and Ministry of Education, Culture and Sport, Government of Spain [Grant number TIN2016-76373-P]. Both are co-funded by the European Regional Development Fund (ERDF).

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Authors and Affiliations

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

    Pedro G. Bascoy, Pablo Quesada-Barriuso, Dora B. Heras & Francisco Argüello

  2. Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany

    Begüm Demir

  3. Department of Information Engineering and Computer Science, University of Trento, Trento, Italy

    Lorenzo Bruzzone

Authors
  1. Pedro G. Bascoy
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  2. Pablo Quesada-Barriuso
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  3. Dora B. Heras
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  4. Francisco Argüello
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  5. Begüm Demir
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  6. Lorenzo Bruzzone
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Corresponding author

Correspondence to Dora B. Heras.

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Bascoy, P.G., Quesada-Barriuso, P., Heras, D.B. et al. Extended attribute profiles on GPU applied to hyperspectral image classification. J Supercomput 75, 1565–1579 (2019). https://doi.org/10.1007/s11227-018-2690-1

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  • DOI: https://doi.org/10.1007/s11227-018-2690-1

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