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Advances in Knowledge Discovery and Management pp 127–145Cite as

A Collaborative Framework for Joint Segmentation and Classification of Remote Sensing Images

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 732))

Abstract

In this article, we present a collaborative framework for joint segmentation and classification. The framework is guided by and aware of the quality of each segment at every stage; it allows the consideration of both homogeneity based criteria as well as implicit semantic criteria to extract the objects belonging to a given thematic class. We apply the proposed framework to vegetation extraction in a very high spatial resolution image of Strasbourg. We compare our results to a pixel-based method, an object-based method and a hybrid segmentation-classification method. The experiments show that the proposed method reaches good classification results while remarkably improving the segmentation results.

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Acknowledgements

The research leading to these results has received funding from the French Agence Nationale de la Recherche (Grant Agreement ANR-12-MONU-0001).

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Authors and Affiliations

  1. iCube UMR CNRS 7357, University of Strasbourg, Strasbourg, France

    Andrés Troya-Galvis & Pierre Gançarski

  2. Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar

    Laure Berti-Équille

Authors
  1. Andrés Troya-Galvis
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  2. Pierre Gançarski
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  3. Laure Berti-Équille
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Corresponding author

Correspondence to Andrés Troya-Galvis .

Editor information

Editors and Affiliations

  1. University of Bordeaux, Bordeaux, France

    Bruno Pinaud

  2. Polytech Nantes, Nantes, France

    Fabrice Guillet

  3. University of Caen Normandie, Caen Cedex 5, France

    Bruno Cremilleux

  4. University of Reims Champagne-Ardenne, Reims, France

    Cyril de Runz

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Troya-Galvis, A., Gançarski, P., Berti-Équille, L. (2018). A Collaborative Framework for Joint Segmentation and Classification of Remote Sensing Images. In: Pinaud, B., Guillet, F., Cremilleux, B., de Runz, C. (eds) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol 732. Springer, Cham. https://doi.org/10.1007/978-3-319-65406-5_6

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  • DOI: https://doi.org/10.1007/978-3-319-65406-5_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65405-8

  • Online ISBN: 978-3-319-65406-5

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