Data interpretation for spectral sensors with correlated bands

Zhipeng Wang, J. Scott Tyo, Majeed M. Hayat

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

New classes of spectral sensors are emerging that have significant overlap in the band spectral response functions. While conventional sensors such as the Multispectral Thermal Images (MTI) or Landsat may have responses with a few percent overlap between adjacent bands, some of the emerging sensors can have more than 50% correlation among all spectral bands. The traditional geometrical models used to describe spectral data fail when such high levels of correlation exist. In this paper we present a generalized geometrical model that relies on functional analysis. We define a sensor space and a scene space that can be used to characterize the suitability of a sensor for a particular spectral sensing task. We demonstrate that classifiers based on first-order distance and angle metrics fail for sensors with highly correlated bands unless appropriate preprocessing is carried out. We further show that second-order statistical classifiers are largely immune to many of the problems introduced by the correlated band responses.

Original languageEnglish (US)
Pages (from-to)2864-2870
Number of pages7
JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
Volume24
Issue number9
DOIs
StatePublished - Sep 2007

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Computer Vision and Pattern Recognition

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