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The polarimetric 𝒢 distribution for SAR data analysis
Author(s) -
Freitas Corina C.,
Frery Alejandro C.,
Correia Antonio H.
Publication year - 2005
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.658
Subject(s) - univariate , clutter , polarimetry , gamma distribution , computer science , remote sensing , interpretability , radar , synthetic aperture radar , laplace distribution , k distribution , multivariate statistics , statistical parameter , exponential distribution , mathematics , statistics , geography , artificial intelligence , physics , machine learning , telecommunications , scattering , optics
Remote sensing data, and radar data in particular, have become an essential tool for environmental studies. Many airborne polarimetric sensors are currently operational, and many more will be available in the near future including spaceborne platforms. The signal‐to‐noise ratio of this kind of imagery is lower than that of optical information, thus requiring a careful statistical modelling. This modelling may lead to useful or useless techniques for image processing and analysis, according to the agreement between the data and their assumed properties. Several distributions have been used to describe synthetic aperture radar (SAR) data. Many of these univariate laws arise by assuming the multiplicative model, such as Rayleigh, square root of gamma, exponential, gamma, and the class of I distributions. The adequacy of these distributions depends on the detection (amplitude, intensity, complex, etc.), the number of looks, and the homogeneity of the data. In Frery et al. (1997), another class of univariate distributions, called , was proposed to model extremely heterogeneous clutter, such as urban areas, as well as other types of clutter. This article extends the univariate family to the multivariate multi‐look polarimetric situation: the p law. The new family has the classical polarimetric multi‐look p distribution as a particular case, but another special case is shown to be more flexible and tractable, while having the same number of parameters and fully retaining their interpretability: the p Olaw. The main properties of this new multivariate distribution are shown. Some results of modelling polarimetric data using the p Odistribution are presented for two airborne polarimetric systems and a variety of targets, showing its expressiveness beyond classical models. Copyright © 2004 John Wiley & Sons, Ltd.