Kernel-Based Linear Spectral Mixture Analysis

Author

Liu, Keng-Hao ; Wong, Englin ; Du, Eliza Yingzi ; Chen, Clayton Chi-Chang ; Chang, Chein-I

Author_Institution

Dept. of Comput. Sci. & Electr. Eng. Dept., Univ. of Maryland, Baltimore, MD, USA

Volume

9

Issue

1

fYear

2012

Firstpage

129

Lastpage

133

Abstract

Linear spectral mixture analysis (LSMA) has been widely used in remote sensing community for spectral unmixing. This letter develops a promising technique, called kernel-based LSMA (KLSMA), which uses nonlinear kernels to resolve the issue of nonlinear separability arising in unmixing and further extends several commonly used LSMA techniques to their kernel-based counterparts. Interestingly, according to experiments conducted for real hyperspectral and multispectral images, KLSMA is more effective than LSMA when data samples are heavily mixed.

Keywords

geophysical image processing; geophysical techniques; remote sensing; spectral analysis; LSMA techniques; hyperspectral image; kernel-based linear spectral mixture analysis; multispectral image; nonlinear kernel analysis; remote sensing; Hyperspectral imaging; Kernel; Principal component analysis; Support vector machines; Training; Fully constrained least squares (FCLS); LSMA; kernel-based linear spectral mixture analysis (LSMA) (KLSMA); least squares orthogonal subspace projection (OSP) (LSOSP); nonnegative constrained least squares (NCLS);

fLanguage

English

Journal_Title

Geoscience and Remote Sensing Letters, IEEE

Publisher

ieee

ISSN

1545-598X

Type

jour

DOI

10.1109/LGRS.2011.2162088

Filename

5989843