Advertisement

Semantic Approach in Image Change Detection

  • Adrien Gressin
  • Nicole Vincent
  • Clément Mallet
  • Nicolas Paparoditis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8192)

Abstract

Change detection is a main issue in various domains, and especially for remote sensing purposes. Indeed, plethora of geospatial images are available and can be used to update geographical databases. In this paper, we propose a classification-based method to detect changes between a database and a more recent image. It is based both on an efficient training point selection and a hierarchical decision process. This allows to take into account the intrinsic heterogeneity of the objects and themes composing a database while limiting false detection rates. The reliability of the designed framework method is first assessed on simulated data, and then successfully applied on very high resolution satellite images and two land-cover databases.

Keywords

change detection updating classification image database 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bruzzone, L., Bovolo, F.: A Novel Framework for the Design of Change-Detection Systems for Very-High-Resolution Remote Sensing Images. Proceedings of the IEEE 101(3), 609–630 (2013)CrossRefGoogle Scholar
  2. 2.
    Buttner, G., et al.: Corine Land Cover update 2000. Technical guidelines. European Environment Agency, Copenhagen (2002)Google Scholar
  3. 3.
    Champion, N., Boldo, D., Pierrot-Deseilligny, M., Stamon, G.: 2D building change detection from high resolution satellite imagery: A two-step hierarchical method based on 3D invariant primitives. PRL 31(10), 1138–1147 (2010)CrossRefGoogle Scholar
  4. 4.
    Demir, B., Minello, L., Bruzzone, L.: An Effective Strategy to Reduce the Labeling Cost in the Definition of Training Sets by Active Learning (2013)Google Scholar
  5. 5.
    Forman, G.: An extensive empirical study of feature selection metrics for text classification. JMLR 3, 1289–1305 (2003)zbMATHGoogle Scholar
  6. 6.
    Gomez-Chova, L., et al.: A review of kernel methods in remote sensing data analysis. In: Optical Remote Sensing, pp. 171–206. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Goyette, N., Jodoin, P.-M., Porikli, F., Konrad, J., Ishwar, P.: A changedetection.net: A new change detection benchmark dataset. In: Proc. IEEE Workshop on Change Detection (CDW 2012) at CVPR 2012, Providence, RI (2012)Google Scholar
  8. 8.
    Le Bris, A.: Extraction of vineyards out of aerial ortho-image using texture information. In: ISPRS Annals of Photogrammetry, Remote Sensing and the Spatial Information Sciences, Melbourne, Australia (2012)Google Scholar
  9. 9.
    Marcal, A., Borges, J., Gomes, J., Pinto Da Costa, J.: Land cover update by supervised classification of segmented ASTER images. IJRS 26(7), 1347–1362 (2005)Google Scholar
  10. 10.
    Miller, O., Pikaz, A., Averbuch, A.: Objects based change detection in a pair of gray-level images. PR 38(11), 1976–1992 (2005)Google Scholar
  11. 11.
    Nemmour, H., Chibani, Y.: Change detector combination in remotely sensed imagery. In: Advanced Concepts for Intelligent Vision Systems Conference, August 31-September 3, pp. 373–380. ACIVS, Brussels (2004)Google Scholar
  12. 12.
    Petitjean, F., Inglada, J., Ganarski, P.: Satellite image time series analysis under time warping. IEEE TGRS 50(8), 3081–3095 (2012)Google Scholar
  13. 13.
    Radke, R.J., Andra, S., Al-Kofahi, O., Roysam, B.: Image change detection algorithms: a systematic survey. IEEE TIP 14(3), 294–307 (2005)MathSciNetGoogle Scholar
  14. 14.
    Robin, A., Moisan, L., Hegarat-Mascle, S.: An a-contrario approach for subpixel change detection in satellite imagery. IEEE TPAMI 32(11), 1977–1993 (2010)CrossRefGoogle Scholar
  15. 15.
    Schölkopf, B., Smola, A.J.: Learning with kernels: support vector machines, regularization, optimization and beyond. The MIT Press (2002)Google Scholar
  16. 16.
    Trias-Sanz, R., Stamon, G., Louchet, J.: Using colour, texture, and hierarchical segmentation for high-resolution remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 63(2), 156–168 (2008)CrossRefGoogle Scholar
  17. 17.
    Wu, T.F., Lin, C.J., Weng, R.C.: Probability estimates for multi-class classification by pairwise coupling. JMLR 5, 975–1005 (2004)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Adrien Gressin
    • 1
    • 2
  • Nicole Vincent
    • 2
  • Clément Mallet
    • 1
  • Nicolas Paparoditis
    • 1
  1. 1.IGN/SR, MATISSaint-MandeFrance
  2. 2.LIPADE - SIPParis-Descartes UniversityParisFrance

Personalised recommendations