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Morphological Recognition of Olive Grove Patterns

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Pattern Recognition and Image Analysis (IbPRIA 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2652))

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Abstract

This paper presents a methodology to segment olive groves in high spatial resolution remotely sensed images. The developed algorithms exploit the typical spatial patterns presented by this forest cover and are mainly based on mathematical morphology operators. It consists on identifying firstly the olive groves followed by the recognition of their individual trees. The methodology is tested with ortophotomaps from a region in central Portugal.

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References

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Author information

Authors and Affiliations

  1. CVRM / Centro de Geo-Sistemas, Instituto Superior Técnico, Av. Rovisco Pais, 1049-001, Lisboa, Portugal

    Teresa Barata & Pedro Pina

Authors
  1. Teresa Barata
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  2. Pedro Pina
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Editor information

Editors and Affiliations

  1. Unitat de Gràfics i Visió per Ordinador Departament de Ciències Matemàtiques i Informàtica, Universitat de les Illes Balears Edifici Anselm Turmeda, Ctra. de Valldemossa km 7,5, 07122, Palma de Mallorca, Spain

    Francisco José Perales

  2. FEUP - Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio J. C. Campilho

  3. Departamento de Ciencias da la Computacíon e I.A., Universidad de Granada, E.T. S. Ing. Informática, 18071, Granada, Spain

    Nicolás Pérez de la Blanca

  4. Dept. System Engineering and Automation, Universitat Politècnica de Catalunya (UPC) Barcelona, Spain

    Alberto Sanfeliu

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© 2003 Springer-Verlag Berlin Heidelberg

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Barata, T., Pina, P. (2003). Morphological Recognition of Olive Grove Patterns. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_11

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  • DOI: https://doi.org/10.1007/978-3-540-44871-6_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40217-6

  • Online ISBN: 978-3-540-44871-6

  • eBook Packages: Springer Book Archive

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