DocumentCode
1885152
Title
Relation between principal components and endmembers in hyperspectral images
Author
Blanco, D. ; Sanchez-Castillo, M. ; Carrión, M.C. ; Tienda-Luna, I.M.
Author_Institution
Dept. of Appl. Phys., Univ. of Granada, Granada, Spain
fYear
2011
fDate
24-29 July 2011
Firstpage
1787
Lastpage
1789
Abstract
In this contribution, the relation between the principal components of the covariance matrix of a hyperspectral image and the spectra of the endmembers is studied. When the data satisfy the spectral mixing model, from this relation the spectra of the endmembers and the abundance of each endmember in the pixels of the image can be theoretically obtained through a non-lineal minimization process. The simple case of an scene with two endmembers is studied using simulations.
Keywords
covariance matrices; geophysical image processing; principal component analysis; covariance matrix; endmembers analysis; hyperspectral images; non-lineal minimization process; principal component analysis; spectral mixing model; Cost function; Covariance matrix; Equations; Estimation; Hyperspectral imaging; Matrix decomposition; Noise; Endmembers Analysis; Hyperspectral images; Principal Component Analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International
Conference_Location
Vancouver, BC
ISSN
2153-6996
Print_ISBN
978-1-4577-1003-2
Type
conf
DOI
10.1109/IGARSS.2011.6049467
Filename
6049467
Link To Document