DocumentCode :
40078
Title :
Sampling Piecewise Convex Unmixing and Endmember Extraction
Author :
Zare, Alina ; Gader, Paul ; Casella, G.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Missouri, Columbia, MO, USA
Volume :
51
Issue :
3
fYear :
2013
fDate :
Mar-13
Firstpage :
1655
Lastpage :
1665
Abstract :
A Metropolis-within-Gibbs sampler for piecewise convex hyperspectral unmixing and endmember extraction is presented. The standard linear mixing model used for hyperspectral unmixing assumes that hyperspectral data reside in a single convex region. However, hyperspectral data are often nonconvex. Furthermore, in standard endmember extraction and unmixing methods, endmembers are generally represented as a single point in the high-dimensional space. However, the spectral signature for a material varies as a function of the inherent variability of the material and environmental conditions. Therefore, it is more appropriate to represent each endmember as a full distribution and use this information during spectral unmixing. The proposed method searches for several sets of endmember distributions. By using several sets of endmember distributions, a piecewise convex mixing model is applied, and given this model, the proposed method performs spectral unmixing and endmember estimation given this nonlinear representation of the data. Each set represents a random simplex. The vertices of the random simplex are modeled by the endmember distributions. The hyperspectral data are partitioned into sets associated with each of the extracted sets of endmember distributions using a Dirichlet process prior. The Dirichlet process prior also estimates the number of sets. Thus, the Metropolis-within-Gibbs sampler partitions the data into convex regions, estimates the required number of convex regions, and estimates endmember distributions and abundance values for all convex regions. Results are presented on real hyperspectral and simulated data that indicate the ability of the method to effectively estimate endmember distributions and the number of sets of endmember distributions.
Keywords :
Markov processes; Monte Carlo methods; feature extraction; geophysical image processing; image sampling; sampling methods; Dirichlet process prior; Metropolis-within-Gibbs sampler; convex regions; data nonlinear representation; endmember distributions; environmental conditions; high-dimensional space; hyperspectral data; piecewise convex hyperspectral endmember extraction sampling; piecewise convex hyperspectral unmixing sampling; single convex region; spectral endmember estimation; spectral unmixing estimation; standard endmember extraction; standard linear mixing model; standard unmixing methods; Covariance matrix; Gaussian distribution; Hyperspectral imaging; Materials; Partitioning algorithms; Vectors; Endmember; Markov chain Monte Carlo (MCMC); hyperspectral; piecewise convex; sampling; spectral variation; unmixing;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/TGRS.2012.2207905
Filename :
6297456
Link To Document :
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