Báo cáo hóa học: " Research Article Scale Mixture of Gaussian Modelling of Polarimetric SAR Data"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Scale Mixture of Gaussian Modelling of Polarimetric SAR Data | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 874592 12 pages doi 2010 874592 Research Article Scale Mixture of Gaussian Modelling of Polarimetric SAR Data Anthony P. Doulgeris and Torbj0rn Eltoft The Department of Physics and Technology University of Tromso 9037 Tromso Norway Correspondence should be addressed to Anthony P. Doulgeris Received 1 June 2009 Accepted 28 September 2009 Academic Editor Carlos Lopez-Martinez Copyright 2010 A. P. Doulgeris and T. Eltoft. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. This paper describes a flexible non-Gaussian statistical method used to model polarimetric synthetic aperture radar POLSAR data. We outline the theoretical basis of the well-know product model as described by the class of Scale Mixture models and discuss their appropriateness for modelling radar data. The statistical distributions of several Scale mixture models are then described including the commonly used Gaussian model and techniques for model parameter estimation are given. Real data evaluations are made using airborne fully polarimetric SAR studies for several distinct land cover types. Generic scale mixture of Gaussian features is extracted from the model parameters and a simple clustering example presented. 1. Introduction It is well known that POLSAR data can be non-Gaussian in nature and that various non-Gaussian models have been used to fit SAR images firstly with single channel amplitude distributions 1-3 and later extended into the polarimetric realm where the multivariate K-distributions 4 5 and G-distributions 6 have been successful. These polarimetric models are derived as stochastic product models 7 8 of a non-Gaussian texture term and a multivariate Gaussianbased speckle term and can be .

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