An unsupervised segmentation method based on MPM for SAR images

被引：27

作者：

Cao, YF ^{[1
]}

Sun, H ^{[1
]}

Xu, X ^{[1
]}

机构：

[1] Wuhan Univ, Sch Elect Informat, Dept Commun Engn, Signal Proc Lab, Wuhan 430079, Peoples R China

来源：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2005年 / 2卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Hidden Markov models; image segmentation; parameter estimation; synthetic aperture radar (SAR);

D O I：

10.1109/LGRS.2004.839649

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

An unsupervised segmentation method for synthetic aperture radar (SAR) images is proposed. It alternately approximates the maximization of the posterior marginals estimate of the pixel class labels and estimates all model parameters except the number of classes during segmentation. In this method, a multilevel logistic (MLL) model for the pixel class labels and Gamma distribution for the marginal distribution of each class in the observed SAR image are employed. In our implementation, the expectation-maximization algorithm is used to estimate parameters of the Gamma distributions, and the iterative conditional estimation algorithm is used to estimate the MLL model parameters. The segmentation results for synthetic and real SAR images show that the proposed method has a good performance.

引用

页码：55 / 58

页数：4

共 8 条

[1] The EM/MPM algorithm for segmentation of textured images: Analysis and further experimental results
Comer, ML
Delp, EJ
[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2000, 9 (10) : 1731 - 1744
[2] MODELING AND SEGMENTATION OF NOISY AND TEXTURED IMAGES USING GIBBS RANDOM-FIELDS
DERIN, H
ELLIOTT, H
[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1987, 9 (01) : 39 - 55
[3] STOCHASTIC RELAXATION, GIBBS DISTRIBUTIONS, AND THE BAYESIAN RESTORATION OF IMAGES
GEMAN, S
GEMAN, D
[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1984, 6 (06) : 721 - 741
[4] Parameter estimation in Markov random field image modeling with imperfect observations.: A comparative study
Ibáñez, MV
Simó, A
[J]. PATTERN RECOGNITION LETTERS, 2003, 24 (14) : 2377 - 2389
[5] PROBABILISTIC SOLUTION OF III-POSED PROBLEMS IN COMPUTATIONAL VISION
MARROQUIN, J
MITTER, S
POGGIO, T
[J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1987, 82 (397) : 76 - 89
[6] Double Markov random fields and Bayesian image segmentation
Melas, DE
Wilson, SP
[J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2002, 50 (02) : 357 - 365
[7] Pieczynski W., 1992, Machine Graphics and Vision, V1, P261
[8] MAXIMUM-LIKELIHOOD PARAMETER-ESTIMATION FOR UNSUPERVISED STOCHASTIC MODEL-BASED IMAGE SEGMENTATION
ZHANG, J
MODESTINO, JW
LANGAN, DA
[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 1994, 3 (04) : 404 - 420

← 1 →