Title :
Clustering of fully polarimetric SAR data using finite Gp0 mixture model and SEM Algorithm
Author :
Horta, Michelle M. ; Mascarenhas, Nelson D A ; Frery, Alejandro C. ; Levada, Alexandre L M
Author_Institution :
Phys. Inst. of Sao Carlos, Univ. of Sao Paulo, Sao Paulo
Abstract :
This paper presents a novel method for clustering multilook polarimetric SAR images by combining the stochastic expectation-maximization (SEM) algorithm with the mixture of Gp 0 distributions, using the method of moments for parameter estimation. The pixel values of multilook SAR data are complex covariance matrices, and they are described by mixtures of gp 0 laws. This distribution can describe different type of targets; like urban areas, forest and pasture. The proposed clustering technique can be applied to unsupervised classification and segmentation process. Experiments with real image data provide good results.
Keywords :
covariance matrices; expectation-maximisation algorithm; image classification; image segmentation; method of moments; parameter estimation; radar imaging; radar polarimetry; stochastic processes; synthetic aperture radar; SAR data clustering; SEM algorithm; clustering multilook polarimetric SAR images; covariance matrix; finite Gp 0 mixture model; method of moments; multilook polarimetric SAR images; parameter estimation; segmentation process; stochastic expectation-maximization algorithm; synthetic aperture radar; unsupervised classification; Clustering algorithms; Covariance matrix; Distributed computing; Image segmentation; Iterative algorithms; Parameter estimation; Physics computing; Speckle; Stochastic processes; Urban areas; Clustering; Finite Mixture Model; Multilook Polarimetric SAR Image; SEM algorithm;
Conference_Titel :
Systems, Signals and Image Processing, 2008. IWSSIP 2008. 15th International Conference on
Conference_Location :
Bratislava
Print_ISBN :
978-80-227-2856-0
Electronic_ISBN :
978-80-227-2880-5
DOI :
10.1109/IWSSIP.2008.4604372
