SAR Image Segmentation Using GHM-Based Dirichlet Process Mixture Models

Author

Sun, Li ; Zhang, Yanning ; Tian, Guangjian ; Ma, Miao

Author_Institution

Sch. of Comput. Sci., Northwestern Polytech. Univ., Xi´´an, China

Volume

1

fYear

2009

fDate

24-26 April 2009

Firstpage

886

Lastpage

888

Abstract

This paper proposes a robust SAR image segmentation scheme for SAR images with speckle noise. Our method can simulate the intrinsic property of SAR image by the proposed infinite mixture model-Dirichlet process mixture model and determine the cluster number automatically. The Gaussian-Hermite moment is applied to extract features to improve the robust of segmentation and reduce the influence of speckle noise. The effectiveness of proposed method is demonstrated via experiments with the simulated data and real data.

Keywords

feature extraction; image segmentation; radar computing; radar imaging; speckle; synthetic aperture radar; Dirichlet process mixture models; GHM; Gaussian-Hermite moment; SAR; feature extraction; image segmentation; infinite mixture model; speckle noise; Clustering algorithms; Computational modeling; Feature extraction; Gaussian noise; Gaussian processes; Image segmentation; Noise reduction; Noise robustness; Polynomials; Speckle; Dirichlet Process; Gaussian Hermite moment; SAR image segmentation;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Sciences and Optimization, 2009. CSO 2009. International Joint Conference on

Conference_Location

Sanya, Hainan

Print_ISBN

978-0-7695-3605-7

Type

conf

DOI

10.1109/CSO.2009.371

Filename

5193834