Nonlinear Estimation of Material Abundances in Hyperspectral Images With l1-Norm Spatial Regularization

被引:69
作者
Chen, Jie [1 ]
Richard, Cedric [1 ]
Honeine, Paul [2 ]
机构
[1] Univ Nice Sophia Antipolis, CNRS, Observ Cote Azur, F-06108 Nice, France
[2] Univ Technol Troyes, CNRS, Inst Charles Delaunay, F-10010 Troyes, France
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2014年 / 52卷 / 05期
关键词
Hyperspectral imaging; l(1)-norm regularization; nonlinear spectral unmixing; spatial regularization; SPECTRAL MIXTURE ANALYSIS; SIGNAL-DEPENDENT NOISE; CLASSIFICATION; EXTRACTION; ALGORITHM;
D O I
10.1109/TGRS.2013.2264392
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Integrating spatial information into hyperspectral unmixing procedures has been shown to have a positive effect on the estimation of fractional abundances due to the inherent spatial-spectral duality in hyperspectral scenes. However, current research works that take spatial information into account are mainly focused on the linear mixing model. In this paper, we investigate how to incorporate spatial correlation into a nonlinear abundance estimation process. A nonlinear unmixing algorithm operating in reproducing kernel Hilbert spaces, coupled with a l(1)-type spatial regularization, is derived. Experiment results, with both synthetic and real hyperspectral images, illustrate the effectiveness of the proposed scheme.
引用
收藏
页码:2654 / 2665
页数:12
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