[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Advertisement

Springer Nature Link
Log in

GPU Framework for Change Detection in Multitemporal Hyperspectral Images

  • Published:
International Journal of Parallel Programming Aims and scope Submit manuscript

Abstract

Nowadays, it is increasingly common to detect land cover changes using remote sensing multispectral images captured at different time-frames over the same area. A large part of the available change detection (CD) methods focus on pixel-based operations. The use of spectral–spatial techniques helps to improve the accuracy results but also implies a significant increase in processing time. In this paper, a Graphic Processor Unit (GPU) framework to perform object-based CD in multitemporal remote sensing hyperspectral data is presented. It is based on Change Vector Analysis with the Spectral Angle Mapper distance and Otsu’s thresholding. Spatial information is taken into account by considering watershed segmentation. The GPU implementation achieves real-time execution and speedups of up to 46.5\(\times \) with respect to an OpenMP implementation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Notes

  1. The datasets along with the reference maps created and some experimental results can be downloaded from: https://wiki.citius.usc.es/hiperespectral:cva.

References

  1. Bannari, A., Morin, D., Bonn, F., Huete, A.: A review of vegetation indices. Remote Sens. Rev. 13(1–2), 95–120 (1995)

    Article  Google Scholar 

  2. Bernabé, S., Sánchez, S., Plaza, A., López, S., Benediktsson, J.A., Sarmiento, R.: Hyperspectral unmixing on GPUs and multi-core processors: a comparison. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 6(3), 1386–1398 (2013)

    Article  Google Scholar 

  3. Bioucas-Dias, J., Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N., Chanussot, J.: Hyperspectral remote sensing data analysis and future challenges. IEEE Geosci. Remote Sens. Mag. 1(2), 6–36 (2013)

    Article  Google Scholar 

  4. Bovolo, F., Bruzzone, L.: The time variable in data fusion: a change detection perspective. IEEE Geosci. Remote Sens. Mag. 3(3), 8–26 (2015)

    Article  Google Scholar 

  5. Bruzzone, L., Prieto, D.F.: Automatic analysis of the difference image for unsupervised change detection. IEEE Trans. Geosci. Remote Sens. 38(3), 1171–1182 (2000)

    Article  Google Scholar 

  6. Celik, T.: Unsupervised change detection in satellite images using principal component analysis and-means clustering. IEEE Geosci. Remote Sens. Lett. 6(4), 772–776 (2009)

    Article  Google Scholar 

  7. CESGA: Finis Terrae II Quick start guide (2017). https://cesga.es/en/paginas/descargaDocumento/id/231

  8. Chen, Z., Vatsavai, R.R., Ramachandra, B., Zhang, Q., Singh, N., Sukumar, S.: Scalable nearest neighbor based hierarchical change detection framework for crop monitoring. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 1309–1314. IEEE (2016)

  9. Christophe, E., Michel, J., Inglada, J.: Remote sensing processing: from multicore to GPU. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 4(3), 643–652 (2011)

    Article  Google Scholar 

  10. Dennison, P.E., Halligan, K.Q., Roberts, D.A.: A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper. Remote Sens. Environ. 93(3), 359–367 (2004)

    Article  Google Scholar 

  11. Falco, N., Mura, M.D., Bovolo, F., Benediktsson, J.A., Bruzzone, L.: Change detection in VHR images based on morphological attribute profiles. Geosci. Remote Sens. Lett. IEEE 10(3), 636–640 (2013)

    Article  Google Scholar 

  12. Fauvel, M., Tarabalka, Y., Benediktsson, J.A., Chanussot, J., Tilton, J.C.: Advances in spectral–spatial classification of hyperspectral images. Proc. IEEE 101(3), 652–675 (2013)

    Article  Google Scholar 

  13. Garea, A.S., Ordóñez, A., Heras, D.B., Argüello, F.: HypeRvieW: an open source desktop application for hyperspectral remote-sensing data processing. Int. J. Remote Sens. 37(23), 5533–5550 (2016)

    Article  Google Scholar 

  14. Ghosh, A., Subudhi, B.N., Bruzzone, L.: Integration of Gibbs Markov random field and Hopfield-type neural networks for unsupervised change detection in remotely sensed multitemporal images. IEEE Trans. Image Process. 22(8), 3087–3096 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  15. Hao, M., Shi, W., Zhang, H., Li, C.: Unsupervised change detection with expectation-maximization-based level set. IEEE Geosci. Remote Sens. Lett. 11(1), 210–214 (2014)

    Article  Google Scholar 

  16. Ke, J., Sowmya, A., Guo, Y., Bednarz, T., Buckley, M.: Efficient GPU computing framework of cloud filtering in remotely sensed image processing. In: 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8. IEEE (2016)

  17. Kempeneers, P., Sedano, F., Strobl, P., McInerney, D.O., San-Miguel-Ayanz, J.: Increasing robustness of postclassification change detection using time series of land cover maps. IEEE Trans. Geosci. Remote Sens. 50(9), 3327–3339 (2012)

    Article  Google Scholar 

  18. Keshava, N.: Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries. IEEE Trans. Geosci. Remote Sens. 42(7), 1552–1565 (2004)

    Article  Google Scholar 

  19. López-Fandiño, J., Quesada-Barriuso, P., Heras, D.B., Argüello, F.: Efficient ELM-based techniques for the classification of hyperspectral remote sensing images on commodity GPUs. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sen. 8(6), 2884–2893 (2015)

    Article  Google Scholar 

  20. Malila, W.A.: Change vector analysis: an approach for detecting forest changes with landsat. In: LARS Symposia, p. 385 (1980)

  21. Nielsen, A.A.: Kernel based orthogonalization for change detection in hyperspectral image data. In: 6th EARSeL Workshop on Imaging Spectroscopy (2013)

  22. Nvidia: CUBLAS Library User Guide (2013)

  23. Nvidia: NVIDIA Tesla P100. The Most Advanced Data Center Accelerator Ever Built. Featuring Pascal P100, the Worlds Fastest GPU (2016)

  24. Otsu, N.: A threshold selection method from gray-level histograms. Automatica 11(285–296), 23–27 (1975)

    Google Scholar 

  25. Pan, W., Qin, K., Chen, Y.: An adaptable-multilayer fractional Fourier transform approach for image registration. IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 400–414 (2009)

    Article  Google Scholar 

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

    Article  Google Scholar 

  27. Plaza, A., Plaza, J., Paz, A., Sanchez, S.: Parallel hyperspectral image and signal processing. IEEE Signal Process. Mag. 28(3), 119–126 (2011)

    Article  Google Scholar 

  28. Quesada-Barriuso, P., Argüello, F., Heras, D.B.: Computing efficiently spectral–spatial classification of hyperspectral images on commodity GPUs. In: Tweedale, J., Jain, L. (eds.) Recent Advances in Knowledge-Based Paradigms and Applications, pp. 19–42. Springer, Berlin (2014)

    Chapter  Google Scholar 

  29. Quesada-Barriuso, P., Heras, D.B., Argüello, F.: Efficient 2D and 3D watershed on graphics processing unit: block-asynchronous approaches based on cellular automata. Comput. Electr. Eng. 39(8), 2638–2655 (2013)

    Article  Google Scholar 

  30. Quesada-Barriuso, P., Heras, D.B., Argüello, F.: Efficient GPU asynchronous implementation of a watershed algorithm based on cellular automata. In: 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications (ISPA), pp. 79–86. IEEE (2012)

  31. Sánchez, S., Ramalho, R., Sousa, L., Plaza, A.: Real-time implementation of remotely sensed hyperspectral image unmixing on GPUs. J. Real Time Image Proc. 10(3), 469–483 (2015)

    Article  Google Scholar 

  32. Singh, A.: Review article digital change detection techniques using remotely-sensed data. Int. J. Remote Sens. 10(6), 989–1003 (1989)

    Article  Google Scholar 

  33. Singh, S., Talwar, R.: Review on different change vector analysis algorithms based change detection techniques. In: 2013 IEEE Second International Conference on Image Information Processing (ICIIP), pp. 136–141. IEEE (2013)

  34. Tarabalka, Y., Benediktsson, J.A., Chanussot, J., Tilton, J.C.: Multiple spectral–spatial classification approach for hyperspectral data. IEEE Trans. Geosci. Remote Sens. 48(11), 4122–4132 (2010)

    Google Scholar 

  35. Van der Meer, F.D., van der Werff, H., van Ruitenbeek, F.J., Hecker, C.A., Bakker, W.H., Noomen, M.F., van der Meijde, M., Carranza, E.J.M., Smeth, J., Woldai, T.: Multi-and hyperspectral geologic remote sensing: a review. Int. J. Appl. Earth Obs. Geoinf. 14(1), 112–128 (2012)

    Article  Google Scholar 

  36. Volpi, M., Tuia, D., Bovolo, F., Kanevski, M., Bruzzone, L.: Supervised change detection in vhr images using contextual information and support vector machines. Int. J. Appl. Earth Obs. Geoinf. 20, 77–85 (2013)

    Article  Google Scholar 

  37. Yang, B., Yang, M., Plaza, A., Gao, L., Zhang, B.: Dual-mode FPGA implementation of target and anomaly detection algorithms for real-time hyperspectral imaging. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(6), 2950–2961 (2015)

    Article  Google Scholar 

  38. Zhang, W., Luo, G., Shen, L., Page, T., Li, P., Jiang, M., Maass, P., Cong, J.: FPGA acceleration by asynchronous parallelization for simultaneous image reconstruction and segmentation based on the Mumford–Shah regularization. In: SPIE Optical Engineering + Applications, p. 96000H. International Society for Optics and Photonics (2015)

  39. Zhu, H., Cao, Y., Zhou, Z., Gong, M.: Parallel multi-temporal remote sensing image change detection on GPU. In: 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), pp. 1898–1904. IEEE (2012)

Download references

Acknowledgements

This work has received financial support from the Ministry of Science and Innovation, Government of Spain, co-funded by the FEDER funds of the European Union, under Contracts TIN2013-41129-P and TIN2016-76373-P; Xunta de Galicia, Programme for Consolidation of Competitive Research Groups Ref. 2014/008; the Consellería de Cultura, Educación e Ordenación Universitaria (Accreditation 2016-2019, ED431G/08); and the European Regional Development Fund (ERDF).

Author information

Authors and Affiliations

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

    Javier López-Fandiño, Dora B. Heras & Francisco Argüello

  2. GIPSA-lab, Institute of Engineering, CNRS, Grenoble INP, Université Grenoble Alpes, 38000, Grenoble, France

    Mauro Dalla Mura

Authors
  1. Javier López-Fandiño
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dora B. Heras
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francisco Argüello
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mauro Dalla Mura
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Javier López-Fandiño.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

López-Fandiño, J., B. Heras, D., Argüello, F. et al. GPU Framework for Change Detection in Multitemporal Hyperspectral Images. Int J Parallel Prog 47, 272–292 (2019). https://doi.org/10.1007/s10766-017-0547-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10766-017-0547-5

Keywords

Search

Navigation