Probabilistic satellite image fusion

Farid Flitti, Mohammed Bennamoun, Du Huynh, Amine Bermak, Christophe Collet

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Remote sensing satellite images play an important role in many applications such as environment and agriculture lands monitoring. In such images the scene is usually observed with different modalities, e.g. wavelengths. Image Fusion is an important analysis tool that summarizes the available information in a unique composite image. This paper proposes a new transform domain image fusion (IF) algorithm based on a hierarchical vector hidden Markov model (HHMM) and the mixture of probabilistic principal component analysers. Results on real Landsat images, quantified subjectively and using objective measures, are very satisfactory.

Original languageEnglish
Title of host publicationComputer Analysis of Images and Patterns - 13th International Conference, CAIP 2009, Proceedings
Pages410-418
Number of pages9
Volume5702 LNCS
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event13th International Conference on Computer Analysis of Images and Patterns, CAIP 2009 - Munster, Germany
Duration: 2 Sep 20094 Sep 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5702 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other13th International Conference on Computer Analysis of Images and Patterns, CAIP 2009
CountryGermany
CityMunster
Period2/9/094/9/09

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Flitti, F., Bennamoun, M., Huynh, D., Bermak, A., & Collet, C. (2009). Probabilistic satellite image fusion. In Computer Analysis of Images and Patterns - 13th International Conference, CAIP 2009, Proceedings (Vol. 5702 LNCS, pp. 410-418). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5702 LNCS). https://doi.org/10.1007/978-3-642-03767-2_50