A gradient based method for fully constrained least-squares unmixing of hyperspectral images

Author

Chen, Jie ; Richard, Cédric ; Lantéri, Henri ; Theys, Céline ; Honeine, Paul

Author_Institution

Inst. Charles Delaunay, Univ. de Technol. de Troyes, Troyes, France

fYear

2011

fDate

28-30 June 2011

Firstpage

301

Lastpage

304

Abstract

Linear unmixing of hyperspectral images is a popular approach to determine and quantify materials in sensed images. The linear unmixing problem is challenging because the abundances of materials to estimate have to satisfy non-negativity and full-additivity constraints. In this paper, we investigate an iterative algorithm that integrates these two requirements into the coefficient update process. The constraints are satisfied at each iteration without using any extra operations such as projections. Moreover, the mean transient behavior of the weights is analyzed analytically, which has never been seen for other algorithms in hyperspectral image unmixing. Simulation results illustrate the effectiveness of the proposed algorithm and the accuracy of the model.

Keywords

geophysical image processing; gradient methods; least squares approximations; remote sensing; coefficient update process; constrained least-squares unmixing; full-additivity constraints; gradient based method; hyperspectral image unmixing; hyperspectral images; iterative algorithm; linear unmixing problem; mean transient behavior; non-negativity constraints; sensed images; Equations; Hyperspectral imaging; Materials; Mathematical model; Pixel; Signal processing algorithms; Hyperspectral imagery; estimation under constraints; linear unmixing;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal Processing Workshop (SSP), 2011 IEEE

Conference_Location

Nice

ISSN

pending

Print_ISBN

978-1-4577-0569-4

Type

conf

DOI

10.1109/SSP.2011.5967687

Filename

5967687