Title :
SAR Image Segmentation Method Using DP Mixture Models
Author :
Li, Sun ; Yanning, Zhang ; Miao, Ma ; Guangjian, Tian
Author_Institution :
Sch. of Comput. Sci., Northwestern Polytech. Univ., Xian, China
Abstract :
This paper presents a new method for segmentation of synthetic aperture radar (SAR) images. Based on a non-parametric Bayesian infinite mixture model, Dirichlet process mixture model cluster method is proposed to segment SAR image. The traditional finite mixture model segmentation method is adapted extensively in SAR image segmentation, but the performance and the robustness is not good enough. However, the proposed infinite mixture model can simulate the intrinsic property of SAR image and the segmentation method can determine the cluster number automatically. The experiment results on the simulated data and real data show that the proposed method gets comparative performance and robustness with the traditional methods.
Keywords :
Bayes methods; boundary-value problems; image segmentation; radar imaging; synthetic aperture radar; DP mixture models; Dirichlet process mixture model cluster method; SAR image segmentation; nonparametric Bayesian infinite mixture model; synthetic aperture radar; Bayesian methods; Clustering algorithms; Computer science; Image segmentation; Optical filters; Optical noise; Parameter estimation; Robustness; Speckle; Synthetic aperture radar; Dirichlet process mixture model; Non-parametric Bayesian model; SAR image segmentation; infinite mixture model;
Conference_Titel :
Computer Science and Computational Technology, 2008. ISCSCT '08. International Symposium on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3746-7
DOI :
10.1109/ISCSCT.2008.20