Spectral mixture analysis of hyperspectral data using Genetic Algorithm and Spectral Angle Constraint (GA-SAC)

Author

Chowdhury, Shuvro ; Jinkai Zhang ; Staenz, Karl ; Peddle, Derek

Author_Institution

Alberta Terrestrial Imaging Centre & Dept. of Geogr., Univ. of Lethbridge, Lethbridge, AB, Canada

fYear

2012

fDate

4-7 June 2012

Firstpage

1

Lastpage

4

Abstract

A Genetic Algorithm and Spectral Angle Constraint (GA-SAC) abundance estimation method is presented to improve the accuracy of abundance maps affected by illumination effects and sensor noise. As an improved version of the Spectral Angle Constraint (SAC), the GA-SAC was compared against several existing techniques. Preliminary results showed advantages of the proposed GA-SAC method from tests involving a simulated hyperspectral data set as well as for mapping mineral abundances using AVIRIS data over Cuprite, Nevada.

Keywords

genetic algorithms; hyperspectral imaging; image denoising; AVIRIS data; Cuprite; GA-SAC abundance estimation method; Nevada; abundance maps; genetic algorithm; hyperspectral data; illumination effects; mineral abundance mapping; sensor noise; spectral angle constraint; spectral mixture analysis; Abstracts; Indexes; Remote sensing; Signal to noise ratio; Sociology; Statistics; Abundance Estimation; Genetic Algorithm; Spectral Angle Mapper; Spectral Unmixing;

fLanguage

English

Publisher

ieee

Conference_Titel

Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2012 4th Workshop on

Conference_Location

Shanghai

Print_ISBN

978-1-4799-3405-8

Type

conf

DOI

10.1109/WHISPERS.2012.6874227

Filename

6874227