Unsupervised SAR image segmentation based on kernel TMFs with belief propagation

Unsupervised SAR image segmentation based on kernel TMFs with belief propagation

The triplet Markov field (TMF) model has achieved promising results in synthetic aperture radar (SAR) image segmentation. Focusing on the simple likelihood modelling of an SAR image and the effective optimisation of the TMF model, an unsupervised SAR image segmentation algorithm based on kernel TMF...

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Bibliographic Details
Main Authors: Lu Gan, Xiaoming Liu, Ziwei Li
Format: Article
Language:English
Published: Wiley 2019-08-01
Series:The Journal of Engineering
Subjects:
synthetic aperture radar
speckle
maximum likelihood estimation
image segmentation
markov processes
radar imaging
simulated sar images
real sar images
triplet markov field model
synthetic aperture radar image segmentation
simple likelihood modelling
effective optimisation
unsupervised sar image segmentation algorithm
complex speckle noise statistical models
piecewise constant likelihood model
kernel mapped space
sar image data
higher dimension space
kernel function
max-product belief propagation algorithm
kernel tmf model
Online Access:https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0428

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https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0428

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