Context-sensitive pan-sharpening of multispectral images

  • Authors:

  • Bruno Aiazzi;Luciano Alparone;Stefano Baronti;Andrea Garzelli;Franco Lotti;Filippo Nencini;Massimo Selva

  • Affiliations:

  • Institute of Applied Physics "Nello Carrara", IFAC-CNR, FI, Italy;Department of Electronics and Telecommunications, University of Florence, Florence, Italy;Institute of Applied Physics "Nello Carrara", IFAC-CNR, FI, Italy;Department of Information Engineering, University of Siena, Siena, Italy;Institute of Applied Physics "Nello Carrara", IFAC-CNR, FI, Italy;Department of Information Engineering, University of Siena, Siena, Italy;Institute of Applied Physics "Nello Carrara", IFAC-CNR, FI, Italy

  • Venue:
  • SAMT'07 Proceedings of the semantic and digital media technologies 2nd international conference on Semantic Multimedia
  • Year:
  • 2007

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Abstract

Multiresolution analysis (MRA) and component substitution (CS) are the two basic frameworks to which image fusion algorithms can be reported when merging multi-spectral (MS) and panchromatic (Pan) images (Pan-sharpening). State-of-the-art algorithms add spatial details derived from the Pan image to the MS bands according to an injection model. The capability of the model to describe the relationship between the MS and Pan images is crucial for the quality of fusion results. Although context adaptive (CA) injection models have been proposed in the framework of MRA, their adoption in CS schemes has been scarcely investigated so far. In this work a CA injection model already tested for MRA algorithms is evaluated also for CS schemes. Qualitative and quantitative results reported for IKONOS high spatial resolution data show that CA injection models are more efficient than global ones.