Statistical characterisation and modelling of SAR images

Authors:
Salim Chitroub;Amrane Houacine;Boualem Sansal
Affiliations:
Signal Processing Laboratory, Electronics Institute, University of Sciences and Technology Houari Boumediene (USTHB), P.O. Box 32, El-Alia, Bab-Ezzouar, Algiers, Algeria;Signal Processing Laboratory, Electronics Institute, University of Sciences and Technology Houari Boumediene (USTHB), P.O. Box 32, El-Alia, Bab-Ezzouar, Algiers, Algeria;Signal Processing Laboratory, Electronics Institute, University of Sciences and Technology Houari Boumediene (USTHB), P.O. Box 32, El-Alia, Bab-Ezzouar, Algiers, Algeria
Venue:
Signal Processing
Year:
2002

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Abstract

Statistical characterisation and modelling of SAR images is of great importance for developing classification algorithms and specialised filters for speckle noise reduction, among other applications. We present here the methods that estimate from the observed data the models that describe their statistical behaviour in a good way. Using the K distribution, the derived models depend on only one parameter whose estimation can be improved by using the bootstrap sampling method coupled with the Monte Carlo technique. An adequate representation of such models in the Pearson system allows physical interpretations. We show also that the K distribution-based models can be deduced through the use of Mellin multiplicative convolution, which has advantage in leading to an easier derivation. To confirm the judicious choice of the K distribution-based models, we provide a comparison with three other models that are often used in the literature.