Paper Info
Title
On the extension of the product model in POLSAR processing for unsupervised classification using information geometry of covariance matrices.
Abstract
We discuss in the paper the use of the Riemannian mean given by the differential geometric tools. This geometric mean is used in this paper for computing the centers of class in the polarimetric H/alpha unsupervised classification process. We can show that the centers of class will remain more stable during the iteration process, leading to a different interpretation of the H/alpha/A classification. This technique can be applied both on classical SCM and on Fixed Point covariance matrices. Used jointly with the Fixed Point CM estimate, this technique can give nice results when dealing with high resolution and highly textured polarimetric SAR images classification.
Year
DOI
Venue
2011
10.1109/IGARSS.2011.6049318
IGARSS
Keywords
Field
DocType
covariance matrices,iterative methods,radar imaging,synthetic aperture radar,Fixed Point covariance matrices,differential geometric tools,high resolution highly textured polarimetric SAR images classification,information geometry,iteration process,polsar processing,product model,unsupervised classification,Classification,Differential Geometry.,Estimation,Polarimetry,SAR
Information geometry,Computer vision,Pattern recognition,Computer science,Synthetic aperture radar,Iterative method,Matrix (mathematics),Differential geometry,Artificial intelligence,Covariance matrix,Fixed point,Covariance
Conference
ISSN
Citations 
PageRank 
2153-6996
1
0.40
References 
Authors
8
5
Name
Order
Citations
PageRank
Pierre Formont121.45
Jean Philippe Ovarlez219025.11
Frédéric Pascal317523.99
Gabriel Vasile414518.88
Laurent Ferro-Famil528945.54