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Fuzzy Regions for Handling Uncertainty in Remote Sensing Image Segmentation

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Computational Science and Its Applications – ICCSA 2008 (ICCSA 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5072))

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Abstract

Increasing availability of satellite imagery is demanding robust image classification methods to ensure a better integration between remote sensing and GIS. Segmentation-based approaches are becoming a popular alternative to traditional pixel-wise methods. Hard segmentation divides an image into a set of non-overlapping image-objects and regularly requires significant user-interaction to parameterise a functional segmentation model. This paper proposes an alternative image segmentation method which outputs fuzzy image-regions expressing degrees of membership to target classes. These fuzzy regions are then defuzzified to derive the eventual land-cover classification. Both steps, fuzzy segmentation and defuzzification, are implemented here using simple statistical learning methods which require very little user input. The new procedure is tested in a land-cover classification experiment in an urban environment. Results show that the method produces good thematic accuracy. It therefore provides a new, automated technique for handling uncertainty in the image analysis process of high resolution imagery.

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Author information

Authors and Affiliations

  1. Cadastral Engineering and Geodesy Department, Universidad Distrital Francisco Jose de Caldas, Bogota, Colombia

    Ivan Lizarazo

  2. School of Geography, Birkbeck College, University of London, London, UK

    Paul Elsner

Authors
  1. Ivan Lizarazo
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  2. Paul Elsner
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Editor information

Osvaldo Gervasi Beniamino Murgante Antonio Laganà David Taniar Youngsong Mun Marina L. Gavrilova

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© 2008 Springer-Verlag Berlin Heidelberg

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Lizarazo, I., Elsner, P. (2008). Fuzzy Regions for Handling Uncertainty in Remote Sensing Image Segmentation. In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2008. ICCSA 2008. Lecture Notes in Computer Science, vol 5072. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69839-5_53

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  • DOI: https://doi.org/10.1007/978-3-540-69839-5_53

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69838-8

  • Online ISBN: 978-3-540-69839-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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