A Bayesian Nonparametric Model Coupled with a Markov Random Field for Change Detection in Heterogeneous Remote Sensing Images

DOI10.1137/15M1047908zbMath1375.94025MaRDI QIDQ5266394

Marie Chabert, Jean-Yves Tourneret, Frédéric P. Pascal, Alain Giros, Jorge Prendes

Publication date: 2 June 2017

Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)

zbMATH Keywords

Markov random fieldchange detectionBayesian nonparametricsynthetic aperture radar imagescollapsed Gibbs sampleroptical images

Mathematics Subject Classification ID

Bayesian inference (62F15) Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Uses Software

• BayesDA

Cites Work

• A tutorial on Bayesian nonparametric models
• Nonparametric Bayesian image segmentation
• Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems
• Ferguson distributions via Polya urn schemes
• Inference from iterative simulation using multiple sequences
• A Bayesian analysis of some nonparametric problems
• Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models
• Partially Collapsed Gibbs Samplers
• A New Multivariate Statistical Model for Change Detection in Images Acquired by Homogeneous and Heterogeneous Sensors
• Bayesian Density Estimation and Inference Using Mixtures
• Integration of Gibbs Markov Random Field and Hopfield-Type Neural Networks for Unsupervised Change Detection in Remotely Sensed Multitemporal Images
• A Theorem about Random Fields
• An invariant form for the prior probability in estimation problems
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