Polarimetric SAR images clustering with Gaussian mixtures model

Author

Azmedroub, Boussad ; Ouarzeddine, Mounira

Author_Institution

Dept. of Telecommun., Univ. of Sci. & Technol. Houari Boumediene, Algiers, Algeria

fYear

2015

fDate

25-27 May 2015

Firstpage

1

Lastpage

5

Abstract

Polarimetric Synthetic Aperture Radar (PolSAR) images offer a possibility to detect the right target backscattering giving a better classification results. In this paper we are interested in polarimetric SAR image clustering by using model-based polarimetric decomposition of Freeman and Yamaguchi like feature vector with the Gaussian Mixture Model (GMM) unsupervised classifier. For the validation of our results we use the polarimetric SAR images of the oberpfaffenhofen site. The results obtained from real data are significant.

Keywords

Gaussian processes; mixture models; radar imaging; radar polarimetry; synthetic aperture radar; Freeman like feature vector; Gaussian mixtures model; Yamaguchi like feature vector; model-based polarimetric decomposition; oberpfaffenhofen site; polarimetric SAR images clustering; polarimetric synthetic aperture radar; target backscattering; unsupervised classifier; Gaussian mixture model; Matrix decomposition; Rough surfaces; Scattering; Surface roughness; Synthetic aperture radar; Gaussian Mixture Model (GMM); Model-based polarimetric decompositions; PolSAR classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Control, Engineering & Information Technology (CEIT), 2015 3rd International Conference on

Conference_Location

Tlemcen

Type

conf

DOI

10.1109/CEIT.2015.7233066

Filename

7233066