Pulse Coupled Neural Networks for Automatic Urban Change Detection at Very High Spatial Resolution

  • Fabio Pacifici
  • William J. Emery
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)

Abstract

In this paper, a novel unsupervised approach based on Pulse-Coupled Neural Networks (PCNNs) for image change detection is discussed. PCNNs are based on the implementation of the mechanisms underlying the visual cortex of small mammals and with respect to more traditional neural networks architectures own interesting advantages. In particular, they are unsupervised and context sensitive. The performance of the algorithm has been evaluated on very high spatial resolution QuickBird and WorldView-1 images. Qualitative and more quantitative results are discussed.

Keywords

Change detection Pulse Coupled Neural Networks Urban Environment 

References

  1. 1.
    Eckhorn, R., Reitboeck, H.J., Arndt, M., Dicke, P.: Feature linking via synchronization among distributed assemblies: simulations of results from cat visual cortex. Neural Computation 2(3), 293–307 (1990)CrossRefGoogle Scholar
  2. 2.
    Kuntimad, G., Ranganath, H.S.: Perfect image segmentation using pulse coupled neural networks. IEEE Transactions on Neural Networks 10(3), 591–598 (1999)CrossRefGoogle Scholar
  3. 3.
    Gu, X., Yu, D., Zhang, L.: Image thinning using pulse coupled neural network. Pattern Recognition Letters 25(9), 1075–1084 (2004)CrossRefGoogle Scholar
  4. 4.
    Karvonen, J.A.: Baltic sea ice sar segmentation and classification using modified pulse-coupled neural networks. IEEE Transactions on Geoscience and Remote Sensing 42(7), 1566–1574 (2004)CrossRefGoogle Scholar
  5. 5.
    Waldemark, K., Lindblad, T., Bečanović, V., Guillen, J.L.L., Klingner, P.: Patterns from the sky satellite image analysis using pulse coupled neural networks for pre-processing, segmentation and edge detection. Pattern Recognition Letters 21(3), 227–237 (2000)CrossRefGoogle Scholar
  6. 6.
    Lindblad, T., Kinser, J.M.: Image processing using pulse-coupled neural networks. Springer, Heidelberg (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Fabio Pacifici
    • 1
  • William J. Emery
    • 2
  1. 1.Department of Computer, Systems and Production EngineeringTor Vergata UniversityRomeItaly
  2. 2.Department of Aerospace Engineering ScienceUniversity of Colorado at BoulderUSA

Personalised recommendations