DocumentCode :
1523071
Title :
PolSAR Data Segmentation by Combining Tensor Space Cluster Analysis and Markovian Framework
Author :
Wang, Yinghua ; Han, Chongzhao ; Tupin, Florence
Author_Institution :
Inst. of Integrated Autom., Xi´´an Jiaotong Univ., Xi´´an, China
Volume :
7
Issue :
1
fYear :
2010
Firstpage :
210
Lastpage :
214
Abstract :
We present a new segmentation method for the fully polarimetric synthetic aperture radar (PolSAR) data by coupling the cluster analysis in the tensor space and the Markov random field (MRF) framework. The PolSAR data are usually obtained as a set of 3 ?? 3 Hermitian positive definite polarimetric covariance matrices, which do not form a Euclidean space. If we regard each matrix as a tensor, the PolSAR data space can be represented as a Riemannian manifold. First, the mean shift algorithm is extended to the manifold to cluster such tensors. Then, under the MRF framework, the data energy term is defined by the memberships of all tensors in all the clusters, and the smoothness energy term is defined according to the cluster overlap rates. These parameters regarding the cluster analysis are computed under the Riemannian framework. The total energy is minimized using a graph-cut-based optimization to achieve the segmentation results. The effectiveness of the proposed method is verified using real fully PolSAR data and synthetic images.
Keywords :
Markov processes; covariance matrices; geophysical image processing; geophysical techniques; image segmentation; pattern clustering; radar polarimetry; remote sensing by radar; synthetic aperture radar; tensors; Hermitian positive definite polarimetric covariance matrix; Markov random field framework; PolSAR data segmentation; Riemannian framework; cluster overlap rates; fully polarimetric synthetic aperture radar; graph-cut-based optimization; image segmentation; tensor space cluster analysis; Cluster analysis; Markov random field (MRF); Riemannian manifold; image segmentation; polarimetric synthetic aperture radar (PolSAR);
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing Letters, IEEE
Publisher :
ieee
ISSN :
1545-598X
Type :
jour
DOI :
10.1109/LGRS.2009.2031660
Filename :
5299029
Link To Document :
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