Nonlinear Spectral Mixture Analysis for Hyperspectral Imagery in an Unknown Environment

Nonlinear spectral mixture analysis for hyperspectral imagery is investigated without prior information about the image scene. A simple but effective nonlinear mixture model is adopted, where the multiplication of each pair of endmembers results in a virtual endmember representing multiple scatterin...

Full description

Saved in:
Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2010-10, Vol.7 (4), p.836-840
Main Authors: Raksuntorn, Nareenart, Qian Du
Format: Article
Language:English
Subjects:
Abundance
Abundance estimation
Errors
hyperspectral imagery
Hyperspectral imaging
Image analysis
Image reconstruction
Imagery
Information analysis
Layout
Neural networks
nonlinear mixture analysis
nonlinear mixture model (NLMM)
Nonlinearity
Pixels
Scattering
Spectra
Spectral analysis
Unknown environments
Vectors
Yield estimation
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Nonlinear spectral mixture analysis for hyperspectral imagery is investigated without prior information about the image scene. A simple but effective nonlinear mixture model is adopted, where the multiplication of each pair of endmembers results in a virtual endmember representing multiple scattering effect during pixel construction process. The analysis is followed by linear unmixing for abundance estimation. Due to a large number of nonlinear terms being added in an unknown environment, the following abundance estimation may contain some errors if most of the endmembers do not really participate in the mixture of a pixel. We take advantage of the developed endmember variable linear mixture model (EVLMM) to search the actual endmember set for each pixel, which yields more accurate abundance estimation in terms of smaller pixel reconstruction error, smaller residual counts, and more pixel abundances satisfying sum-to-one and nonnegativity constraints.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2010.2049334