Hierarchical Segmentation of Multiresolution Remote Sensing Images

  • Camille Kurtz
  • Nicolas Passat
  • Anne Puissant
  • Pierre Gançarski
Conference paper

DOI: 10.1007/978-3-642-21569-8_30

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6671)
Cite this paper as:
Kurtz C., Passat N., Puissant A., Gançarski P. (2011) Hierarchical Segmentation of Multiresolution Remote Sensing Images. In: Soille P., Pesaresi M., Ouzounis G.K. (eds) Mathematical Morphology and Its Applications to Image and Signal Processing. ISMM 2011. Lecture Notes in Computer Science, vol 6671. Springer, Berlin, Heidelberg

Abstract

The extraction of urban patterns from very high spatial resolution optical images presents challenges related to the size, the accuracy and the complexity of the data. In order to efficiently carry out this task, a multiresolution hierarchical approach is proposed. It enables to progressively segment several images (of increasing resolutions) of a same scene, based on low level criteria. The process, based on binary partition trees, is partially performed in an interactive fashion, and then automatically completed. Experiments on urban images datasets provide encouraging results which may be further used for detection and classification purpose.

Keywords

Hierarchical segmentation multisource images multiresolution interactive/automated segmentation partition-trees remote sensing urban analysis 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Camille Kurtz
    • 1
  • Nicolas Passat
    • 1
  • Anne Puissant
    • 2
  • Pierre Gançarski
    • 1
  1. 1.LSIIT, UMR CNRS 7005Université de StrasbourgStrasbourgFrance
  2. 2.LIVE, ERL CNRS 7230Université de StrasbourgStrasbourgFrance

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