Semantic Supervised Clustering Approach to Classify Land Cover in Remotely Sensed Images

  • Miguel Torres
  • Marco Moreno
  • Rolando Menchaca-Mendez
  • Rolando Quintero
  • Giovanni Guzman
Conference paper

DOI: 10.1007/978-3-642-17641-8_10

Part of the Communications in Computer and Information Science book series (CCIS, volume 123)
Cite this paper as:
Torres M., Moreno M., Menchaca-Mendez R., Quintero R., Guzman G. (2010) Semantic Supervised Clustering Approach to Classify Land Cover in Remotely Sensed Images. In: Kim T., Pal S.K., Grosky W.I., Pissinou N., Shih T.K., Ślęzak D. (eds) Signal Processing and Multimedia. Communications in Computer and Information Science, vol 123. Springer, Berlin, Heidelberg

Abstract

GIS applications involve applying classification algorithms to remotely sensed images to determine information about a specific region on the Earth’s surface. These images are very useful sources of geographical data commonly used to classify land cover, analyze crop conditions, assess mineral and petroleum deposits and quantify urban growth. In this paper, we propose a semantic supervised clustering approach to classify multispectral information in satellite images. We use the maximum likelihood method to generate the clustering. In addition, we complement the analysis applying spatial semantics to determine the training sites and refine the classification. The approach considers the a priori knowledge of the remotely sensed images to define the classes related to the geographic environment. In this case, the properties and relations that involve the geo-image define the spatial semantics; these features are used to determine the training data sites. The method attempts to improve the supervised clustering, adding the intrinsic semantics of multispectral satellite images in order to establish the classes that involve the analysis with more precision.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Miguel Torres
    • 1
  • Marco Moreno
    • 1
  • Rolando Menchaca-Mendez
    • 1
  • Rolando Quintero
    • 1
  • Giovanni Guzman
    • 1
  1. 1.Intelligent Processing of Geospatial Information LaboratoryComputer Research Center, National Polytechnic InstituteMexico CityMexico

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