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A Possibility-Based Model to Index Remote Sensing Images

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Spatio-Temporal Databases

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Abstract

Recently, several environmental applications took advantage of the use and analysis of remote sensing images, since they are intrinsically referred to the spatial distribution of the phenomena of interest. Remote sensing is widely employed for land monitoring, land planning and risk prevention; its ultimate aim is to identify specific image contents to create thematic maps. An efficient management of large collections of remote sensing images and effective retrieval mechanisms are therefore becoming a need, so that remote sensing images have been included in the list of Grand Challenges application fields for visual information management systems or Content-Based Information Retrieval (CBIR) systems [24]: in fact this application area justifies the use of large computers and storage capacity necessary for visual databases.

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Author information

Authors and Affiliations

  1. IREA-CNR, Via Bassini 15, I-20133, Milan, Italy

    Paola Carrara, Monica Pepe & Anna Rampini

  2. ITC-CNR, Via Bassini 15, I-20133, Milan, Italy

    Gabriella Pasi

Authors
  1. Paola Carrara
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  2. Gabriella Pasi
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  3. Monica Pepe
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  4. Anna Rampini
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Editor information

Editors and Affiliations

  1. Dept. of Telecommunications and Information Processing, Computer Science Laboratory, Ghent University, Sint-Pietersnieuwstraat 41, 9000, Ghent, Belgium

    Rita de Caluwe  & Guy de Tré  & 

  2. CNR IDPA — Institute for Environmental Process Dynamics, Italian National Research Council, Via Pasubio 5, 24044, Dalmine (BG), Italy

    Gloria Bordogna

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© 2004 Springer-Verlag Berlin Heidelberg

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Carrara, P., Pasi, G., Pepe, M., Rampini, A. (2004). A Possibility-Based Model to Index Remote Sensing Images. In: de Caluwe, R., de Tré, G., Bordogna, G. (eds) Spatio-Temporal Databases. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-09968-1_13

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  • DOI: https://doi.org/10.1007/978-3-662-09968-1_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-06070-0

  • Online ISBN: 978-3-662-09968-1

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