DocumentCode :
576146
Title :
Segmentation of polarimetric SAR data with a multi-texture product model
Author :
Doulgeris, A.P. ; Anfinsen, S.N. ; Eltoft, T.
Author_Institution :
Dept. of Phys. & Technol., Univ. of Tromso, Tromso, Norway
fYear :
2012
fDate :
22-27 July 2012
Firstpage :
1437
Lastpage :
1440
Abstract :
The previously proposed multi-texture model for multi-looked PolSAR data statistics [1] is hereby implemented into an advanced statistical clustering algorithm and tested on several real PolSAR images. The multi-texture model is based on the product model for SAR statistics, yet allows the possibility of different texture parameters for the co-polarized (co-pol) and cross-polarized (cross-pol) channels. The implementation automatically determines the most appropriate texture model between the proposed “dual-texture” model and the traditional “scalar-texture” model. The clustering algorithm is implemented as a multi-texture version of [2]. It incorporates the flexible U-distribution, contextual smoothing with Markov random fields, and determines the number of classes with goodness-of-fit tests. The real SAR examples indicate that multi-texture is not generally required and we discuss the possible mis-interpretation of multi-texture in alternative window-based estimation methods, due to mixing of different polarimetric classes.
Keywords :
Markov processes; geophysical image processing; image segmentation; image texture; pattern clustering; polarisation; radar imaging; radar polarimetry; synthetic aperture radar; Markov random field; PolSAR data statistics; copolarized channel; crosspolarized channel; flexible U-distribution; image segmentation; image texture model; multitexture product model; polarimetric SAR; radar polarimetry; scalar texture model; statistical clustering algorithm; Clustering algorithms; Context modeling; Covariance matrix; Data models; Histograms; Image segmentation; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International
Conference_Location :
Munich
ISSN :
2153-6996
Print_ISBN :
978-1-4673-1160-1
Electronic_ISBN :
2153-6996
Type :
conf
DOI :
10.1109/IGARSS.2012.6351265
Filename :
6351265
Link To Document :
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