Restricted total least squares solutions for hyperspectral imagery

Published

Conference Paper

© 2000 IEEE. Hyperspectral image processing is a pixel-by-pixel approach to the detection and localization of features by spectral analysis techniques. Usually, partial knowledge about the feature, noise, and clutter spectra are provided, and the problem is to 'unmix' each pixel, or to estimate the relative concentrations of the reference spectra on a per pixel basis. A popular method of linear spectral unmixing for hyperspectral imagery is linear least squares. Linear least square approaches are appropriate when observational errors predominate and are inappropriate when significant modeling errors are present. The least square approach has some disadvantages, especially in cases with few, poorly known references or significant reference variation throughout an image. approach is presented and evaluated on experimental data. Although proposed RTLS require more calculations than linear least squares, its relative error performance is much better. In this article, Restricted Total Least Squares(RTLS).

Full Text

Duke Authors

Cited Authors

  • Sirlceci, B; Brady, D; Burman, J

Published Date

  • January 1, 2000

Published In

Volume / Issue

  • 1 /

Start / End Page

  • 624 - 627

International Standard Serial Number (ISSN)

  • 1520-6149

International Standard Book Number 10 (ISBN-10)

  • 0780362934

Digital Object Identifier (DOI)

  • 10.1109/ICASSP.2000.862059

Citation Source

  • Scopus