A Bayesian framework for abundance estimation in hyperspectral data using Markov random fields

被引：0
|
作者：
Stites, Matthew R. [1 ]
Moon, Todd K. [1 ]
Gunther, Jacob H. [1 ]
Williams, Gustavious P. [2 ]
机构：
[1] Utah State Univ, ECE Dept, Logan, UT 84322 USA
[2] Brigham Young Univ, Dept Civil Engn, Provo, UT 84602 USA
来源：
CONFERENCE RECORD OF THE FORTY-FIRST ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS, VOLS 1-5 | 2007年
关键词：
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
A model is proposed which uses neighborhoods of pixels as priors in a Bayesian setting to extract abundance information from a hyperspectral image. It is assumed that elements of the abundance vector for a pixel are independent, but that corresponding elements of abundance vectors for neighboring pixels are correlated. A posterior density encourages estimated abundances in neighboring pixels to be similar. Minimum mean-square error estimates are obtained by averaging samples from this density, where the samples are obtained by Gibbs sampling.
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页码：725 / +
页数：2
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