A Multiscale Change Detection Technique Robust to Registration Noise

  • Lorenzo Bruzzone
  • Francesca Bovolo
  • Silvia Marchesi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4815)


This paper addresses the problem of unsupervised change detection in multitemporal very high geometrical resolution remote sensing images. In particular, it presents a study on the effects and the properties of the registration noise on the change-detection process in the framework of the polar change vector analysis (CVA) technique. According to this study, a multiscale technique for reducing the impact of residual misregistration in unsupervised change detection is presented. This technique is based on a differential analysis of the direction distributions of spectral change vectors at different resolution levels. The differential analysis allows one to discriminate sectors associated with residual registration noise from sectors associated with true changes. The information extracted is used at full resolution for computing a change-detection map where geometrical details are preserved and the impact of residual registration noise is strongly reduced.


Change detection change vector analysis registration noise multi-temporal images very high geometrical resolution images multiscale techniques remote sensing 


  1. 1.
    Singh, A.: Digital change detection techniques using remotely-sensed data. Int. J. Rem. Sens. 10(6), 989–1003 (1989)CrossRefGoogle Scholar
  2. 2.
    Bovolo, F., Bruzzone, L.: A Theoretical Framework for Unsupervised Change Detection Based on Change Vector Analysis in Polar Domain. IEEE Transactions on Geoscience and Remote Rensing 45(1), 218–236 (2007)CrossRefGoogle Scholar
  3. 3.
    Dai, X., Khorram, S.: The effects of image misregistration on the accuracy of remotely sensed change detection. IEEE Transactions on Geoscience and Remote Sensing 36, 1566–1577 (1998)CrossRefGoogle Scholar
  4. 4.
    Townshend, J.R.G., Justice, C.O., Gurney, C.: The impact of misregistration on change detection. IEEE Transactions on Geoscience and Remote Sensing 30, 1054–1060 (1992)CrossRefGoogle Scholar
  5. 5.
    Bruzzone, L., Cossu, R.: An adaptive approach for reducing registration noise effects in unsupervised change detection. IEEE Transactions on Geoscience and Remote Sensing 41(11), 2455–2465 (2003)CrossRefGoogle Scholar
  6. 6.
    ENVI User Manual. Boulder, CO: RSI (2003),
  7. 7.
    Bovolo, F., Bruzzone, L.: A detail-preserving scale-driven approach to change detection in multitemporal SAR images. IEEE Transactions on Geoscience and Remote Sensing 43(12), 2963–2972 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Lorenzo Bruzzone
    • 1
  • Francesca Bovolo
    • 1
  • Silvia Marchesi
    • 1
  1. 1.Department of Information and Communication Technologies, Via Sommarive, 14, I-38050 TrentoItaly

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