A new multi-spectral feature level image fusion method for human interpretation

Marom Leviner, Masha Maltz

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

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 a three-task experiment using MSSF against two established methods: averaging and principle components analysis (PCA), and against its two source bands, visible and infrared. The three tasks that we studied were: (1) simple target detection, (2) spatial orientation, and (3) camouflaged target detection. MSSF proved superior to the other fusion methods in all three tests; MSSF also outperformed the source images in the spatial orientation and camouflaged target detection tasks. 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
Pages (from-to)79-88
Number of pages10
JournalInfrared Physics and Technology
Volume52
Issue number2-3
DOIs
StatePublished - 1 Mar 2009

Keywords

  • Camouflage
  • Image fusion
  • Infrared
  • Multispectral
  • Spatial orientation
  • Target detection

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