In *Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)*, volume 2905, pages 129-136, 2003.

abstract bibtex

abstract bibtex

The precise knowledge of the statistical properties of synthetic aperture radar (SAR) data plays a central role in image processing and understanding. These properties can be used for discriminating types of land uses and to develop specialized filters for speckle noise reduction, among other applications. In this work we assume the distribution GA0 as the universal model for multilook amplitude SAR images under the multiplicative model. We study some important properties of this distribution and some classical estimators for its parameters, such as Maximum Likelihood (ML) estimators, but they can be highly influenced by small percentages of 'outliers', i.e., observations that do not fully obey the basic assumptions. Hence, it is important to find Robust Estimators. One of the best known classes of robust techniques is that of M estimators, which are an extension of the ML estimation method. We compare those estimation procedures by means of a Monte Carlo experiment. © Springer-Verlag Berlin Heidelberg 2003.

@inproceedings{35248856088, abstract = "The precise knowledge of the statistical properties of synthetic aperture radar (SAR) data plays a central role in image processing and understanding. These properties can be used for discriminating types of land uses and to develop specialized filters for speckle noise reduction, among other applications. In this work we assume the distribution GA0 as the universal model for multilook amplitude SAR images under the multiplicative model. We study some important properties of this distribution and some classical estimators for its parameters, such as Maximum Likelihood (ML) estimators, but they can be highly influenced by small percentages of 'outliers', i.e., observations that do not fully obey the basic assumptions. Hence, it is important to find Robust Estimators. One of the best known classes of robust techniques is that of M estimators, which are an extension of the ML estimation method. We compare those estimation procedures by means of a Monte Carlo experiment. © Springer-Verlag Berlin Heidelberg 2003.", year = "2003", title = "Robust estimation of roughness parameter in SAR amplitude images", volume = "2905", keywords = "Monte Carlo , Robust Estimation , SAR Images , Speckle Noise", pages = "129-136", booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)", author = "Allende, Héctor and Pizarro, Luis" }

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