Publications [#330755] of David J. Brady
- Sirlceci, B; Brady, D; Burman, J, Restricted total least squares solutions for hyperspectral imagery,
2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp), vol. 1
pp. 624-627, IEEE, ISBN 0780362934 [doi]
(last updated on 2019/11/13)
© 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).