A Novel Method for Fast Processing of Large Remote Sensed Image

  • Adriano Mancini
  • Anna Nora Tassetti
  • Alessandro Cinnirella
  • Emanuele Frontoni
  • Primo Zingaretti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)

Abstract

In this paper we present a novel approach to reduce the computational load of a CFAR detector. The proposed approach is based on the use of integral images to directly manage the presence of masked pixels or invalid data and reduce the computational time. The approach goes through the challenging problem of ship detection from remote sensed data. The capability of fast image processing allows to monitor the marine traffic and identify possible threats. The approach allows to significantly boost the performance up to 50x working with very high resolution image and large kernels.

Keywords

Remote Sensing CFAR VHR imagery SAR ship detection integral image 

References

  1. 1.
    Declims eu project website, https://declims.jrc.ec.europa.eu/home
  2. 2.
    Next esa sar toolbox - array, http://nest.array.ca/web/nest
  3. 3.
    Wackerman, C., et al.: Toward an automated ship and wake detection system (2006)Google Scholar
  4. 4.
    Barale, V., Gade, M.: Remote Sensing of the European Seas. Springer Science Business Media B.V. (2008), http://books.google.fr/books?id=9B3D5-HBTzkC
  5. 5.
    Corbane, C., Najman, L., Pecoul, E., Demagistri, L., Petit, M.: A complete processing chain for ship detection using optical satellite imagery. Int. J. Remote Sens. 31(22), 5837–5854 (2010), http://dx.doi.org/10.1080/01431161.2010.512310 CrossRefGoogle Scholar
  6. 6.
    Crisp, D., Science, D., Laboratory, T.O.A.I.S.: The State-of-the-art in Ship Detection in Synthetic Aperture Radar Imagery. Research report (Defence Science and Technology Organisation (Australia). DSTO Information Sciences Laboratory (2004), http://books.google.it/books?id=cdGvtwAACAAJ
  7. 7.
    Engdahl, M., Minchella, A., Marinkovic, P., Veci, L., Lu, J.: Nest: An esa open source toolbox for scientific exploitation of sar data. In: 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 5322–5324 (2012)Google Scholar
  8. 8.
    Frost, V.S., Stiles, J.A., Shanmugan, K., Holtzman, J.: A model for radar images and its application to adaptive digital filtering of multiplicative noise. Pattern Analysis and Machine Intelligence, IEEE Transactions on PAMI-4(2), 157–166 (1982)Google Scholar
  9. 9.
    Huang, G., Wang, Y., Zhang, Y., Tian, Y.: Ship detection using texture statistics from optical satellite images. In: 2011 International Conference on Digital Image Computing Techniques and Applications (DICTA), pp. 507–512 (2011)Google Scholar
  10. 10.
    Huang, W., Chen, P., Yang, J., Fu, B., Xiao, Q., Yao, L., Zhou, C.: An improved cfar model for ship detection in sar imagery. In: Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2004, vol. 7, pp. 4719–4722 (2004)Google Scholar
  11. 11.
    Kuan, D.T., Sawchuk, A., Strand, T.C., Chavel, P.: Adaptive restoration of images with speckle. IEEE Transactions on Acoustics, Speech and Signal Processing 35(3), 373–383 (1987)CrossRefGoogle Scholar
  12. 12.
    Lee, J.S.: Speckle suppression and analysis for synthetic aperture radar images. Optical Engineering 25(5), 255636–255636 (1986)CrossRefGoogle Scholar
  13. 13.
    Lopes, A., Nezry, E., Touzi, R., Laur, H.: Maximum a posteriori speckle filtering and first order texture models in sar images. In: 10th Annual International Geoscience and Remote Sensing Symposium, IGARSS 1990. ‘Remote Sensing Science for the Nineties’, pp. 2409–2412 (1990)Google Scholar
  14. 14.
    Shafait, F., Keysers, D., Breuel, T.M.: Efficient implementation of local adaptive thresholding techniques using integral images, 681510–681510-6 (2008)Google Scholar
  15. 15.
    Tunaley, J.: Ship detection in sar imagery. Tech. rep., LRDC Technical Report (December 2010)Google Scholar
  16. 16.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, pp. I-511–I-518 (2001)Google Scholar
  17. 17.
    Willhauck, G., et al.: Object-oriented ship detection from vhr satellite images. Tech. rep. (2005)Google Scholar
  18. 18.
    Zhu, C., Zhou, H., Wang, R., Guo, J.: A novel hierarchical method of ship detection from spaceborne optical image based on shape and texture features. IEEE Transactions on Geoscience and Remote Sensing 48(9), 3446–3456 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Adriano Mancini
    • 1
  • Anna Nora Tassetti
    • 2
  • Alessandro Cinnirella
    • 3
  • Emanuele Frontoni
    • 1
  • Primo Zingaretti
    • 1
  1. 1.Dipartimento di Ingegneria dell’InformazioneUniversità Politecnica delle Marche AnconaItaly
  2. 2.Dipartimento di Ingegneria Civile, Edile e ArchitetturaUniversit‘a Politecnica delle Marche AnconaItaly
  3. 3.Dipartimento di Scienze GeologicheUniversità degli Studi Roma TreItaly

Personalised recommendations