Interferometric SAR image restoration using Monte Carlo metropolis method

2002 ◽  
Vol 50 (2) ◽  
pp. 290-298 ◽  
Author(s):  
A.B. Suksmono ◽  
A. Hirose
Keyword(s):  
Monte Carlo ◽  
Image Restoration ◽  
Sar Image ◽  
Metropolis Method ◽  
Interferometric Sar

scholarly journals A sequential Bayesian approach for the estimation of the age–depth relationship of Dome Fuji ice core

2015 ◽  
Vol 2 (3) ◽  
pp. 939-968
Author(s):  
S. Nakano ◽  
K. Suzuki ◽  
K. Kawamura ◽  
F. Parrenin ◽  
T. Higuchi
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Sequential Monte Carlo ◽  
Ice Core ◽  
Posterior Distributions ◽  
Metropolis Method ◽  
Depth Relationship ◽  
Relationship Of ◽  
Thinning Process

Abstract. A technique for estimating the age–depth relationship in an ice core and evaluating its uncertainty is presented. The age–depth relationship is mainly determined by the accumulation of snow at the site of the ice core and the thinning process due to the horizontal stretching and vertical compression of ice layers. However, since neither the accumulation process nor the thinning process are fully understood, it is essential to incorporate observational information into a model that describes the accumulation and thinning processes. In the proposed technique, the age as a function of depth is estimated from age markers and δ18O data. The estimation is achieved using the particle Markov chain Monte Carlo (PMCMC) method, in which the sequential Monte Carlo (SMC) method is combined with the Markov chain Monte Carlo method. In this hybrid method, the posterior distributions for the parameters in the models for the accumulation and thinning processes are computed using the Metropolis method, in which the likelihood is obtained with the SMC method. Meanwhile, the posterior distribution for the age as a function of depth is obtained by collecting the samples generated by the SMC method with Metropolis iterations. The use of this PMCMC method enables us to estimate the age–depth relationship without assuming either linearity or Gaussianity. The performance of the proposed technique is demonstrated by applying it to ice core data from Dome Fuji in Antarctica.

scholarly journals SAR Image Restoration From Spectrum Aliasing by Deep Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 40367-40377
Author(s):  
ZHE LIU ◽  
Ning Wu ◽  
Xingxing Liao
Keyword(s):  
Deep Learning ◽  
Image Restoration ◽  
Sar Image

Analytic Search Method for Interferometric SAR Image Registration

2008 ◽  
Vol 5 (2) ◽  
pp. 294-298 ◽  
Author(s):  
Bao-Quan Liu ◽  
Da-Zheng Feng ◽  
Peng-Lang Shui ◽  
Nan Wu
Keyword(s):  
Image Registration ◽  
Search Method ◽  
Sar Image ◽  
Interferometric Sar

A HIERARCHICAL BAYESIAN ANALYSIS OF HORSE RACING

2013 ◽  
Vol 6 (3) ◽  
pp. 1-13
Author(s):  
Noah Silverman
Keyword(s):  
Monte Carlo ◽  
Bayesian Methods ◽  
Bayesian Method ◽  
Popular Literature ◽  
Horse Racing ◽  
Running Speed ◽  
Hierarchical Bayesian Analysis ◽  
Metropolis Method ◽  
The World ◽  
The Subject

Horse racing is the most popular sport in Hong Kong. Nowhere else in the world is such attention paid to the races and such large sums of money bet. It is literally a “national sport”. Popular literature has many stories about computerized “betting teams” winning fortunes by using statistical analysis.[1] Additionally, numerous academic papers have been published on the subject, implementing a variety of statistical methods. The academic justification for these papers is that a parimutuel game represents a study in decisions under uncertainty, efficiency of markets, and even investor psychology. A review of the available published literature has failed to find any Bayesian approach to this modeling challenge.This study will attempt to predict the running speed of a horse in a given race. To that effect, the coefficients of a linear model are estimated using the Bayesian method of Markov Chain Monte Carlo. Two methods of computing the sampled posterior are used and their results compared. The Gibbs method assumes that all the coefficients are normally distributed, while the Metropolis method allows for their distribution to have an unknown shape. I will calculate and compare the predictive results of several models using these Bayesian Methods.

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