Unsupervised classification of PolSAR data using Freeman decomposition and fuzzy clustering

Author

Tan, Lulu ; Yang, Ruliang

Author_Institution

Inst. of Electron., Chinese Acad. of Sci., Beijing, China

fYear

2009

fDate

26-30 Oct. 2009

Firstpage

489

Lastpage

493

Abstract

An unsupervised classification method using Freeman decomposition and fuzzy clustering is proposed to solve the ambiguity problem among surface, volume and double-bounce scattering dominated region. A fuzzy clustering method of PolSAR data making use of scattering power entropy and anisotropy parameters is proposed to partition different scattering mechanisms dominated region. The proposed method is applied to full polarimetric synthetic aperture radar data of Oberpfaffenhofen acquired by ESAR. Experiment result confirms the validity of this method.

Keywords

entropy; fuzzy set theory; radar polarimetry; scattering; synthetic aperture radar; Freeman decomposition; PolSAR data; anisotropy parameters; double-bounce scattering dominated region; fuzzy clustering; polarimetric synthetic aperture radar; scattering power entropy; unsupervised classification method; Anisotropic magnetoresistance; Classification algorithms; Clustering methods; Covariance matrix; Data mining; Entropy; Pixel; Polarimetric synthetic aperture radar; Radar scattering; Synthetic aperture radar; Freeman decomposition; PolSAR; fuzzy clustering; unsupervised classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Synthetic Aperture Radar, 2009. APSAR 2009. 2nd Asian-Pacific Conference on

Conference_Location

Xian, Shanxi

Print_ISBN

978-1-4244-2731-4

Electronic_ISBN

978-1-4244-2732-1

Type

conf

DOI

10.1109/APSAR.2009.5374123

Filename

5374123