Unsupervised SAR Image Segmentation Using a Hierarchical TMF Model

Author

Peng Zhang ; Ming Li ; Yan Wu ; Gaofeng Liu ; Hongmeng Chen ; Lu Jia

Author_Institution

Nat. Key Lab. of Radar Signal Process., Xidian Univ., Xian, China

Volume

10

Issue

5

fYear

2013

fDate

Sept. 2013

Firstpage

971

Lastpage

975

Abstract

The triplet Markov field (TMF) model recently proposed is suitable for tackling the nonstationary image segmentation. In this letter, we propose a hierarchical TMF (HTMF) model for unsupervised synthetic aperture radar (SAR) image segmentation. In virtue of the Bayesian inference on the quadtree, the HTMF model captures the global and local image characteristics more precisely in the bottom-up and top-down probability computations. In this way, the underlying spatial structure information is effectively propagated. To model the SAR data related to radar backscattering sources, generalized Gamma distribution is utilized. The effectiveness of the proposed HTMF model is demonstrated by application to simulated data and real SAR image segmentation.

Keywords

Bayes methods; Markov processes; geophysical image processing; image segmentation; remote sensing by radar; synthetic aperture radar; Bayesian inference; bottom-up probability computation; hierarchical TMF model; nonstationary image segmentation; synthetic aperture radar; top-down probability computation; triplet Markov field model; unsupervised SAR image segmentation; Adaptation models; Bayesian methods; Computational modeling; Data models; Image segmentation; Markov processes; Synthetic aperture radar; Bayesian inference; hierarchical triplet Markov field (HTMF) model; multiclass segmentation; synthetic aperture radar (SAR) image;

fLanguage

English

Journal_Title

Geoscience and Remote Sensing Letters, IEEE

Publisher

ieee

ISSN

1545-598X

Type

jour

DOI

10.1109/LGRS.2012.2227295

Filename

6400285