Multispectral image fusion for target detection

Marom Leviner, Masha Maltz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


Various different methods to perform multi-spectral image fusion have been suggested, mostly on the pixel level. However, the jury is still out on the benefits of a fused image compared to its source images. We present here a new multi-spectral image fusion method, multi-spectral segmentation fusion (MSSF), which uses a feature level processing paradigm. To test our method, we compared human observer performance in an experiment using MSSF against two established methods: Averaging and Principle Components Analysis (PCA), and against its two source bands, visible and infrared. The task that we studied was: target detection in the cluttered environment. MSSF proved superior to the other fusion methods. Based on these findings, current speculation about the circumstances in which multi-spectral image fusion in general and specific fusion methods in particular would be superior to using the original image sources can be further addressed.

Original languageEnglish
Title of host publicationElectro-Optical and Infrared Systems
Subtitle of host publicationTechnology and Applications VI
StatePublished - 4 Nov 2009
EventElectro-Optical and Infrared Systems: Technology and Applications VI - Berlin, Germany
Duration: 31 Aug 20093 Sep 2009

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


ConferenceElectro-Optical and Infrared Systems: Technology and Applications VI


  • Image fusion
  • Infrared images
  • Multispectral
  • Target detection

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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