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 \(\mathcal{G}^{0}_{A}\) 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.
Chapter PDF
Similar content being viewed by others
References
Allende, H., Frery, A.C., Galbiati, J., Pizarro, L.: M-Estimators with Asymmetric Influence Functions: the \(\mathcal{G}^{0}_{A}\) Distribution Case. Technical Report 2003/5, Departamento de Informática, Universidad Técnica Federico Santa María. Submitted to Computational Statistics and Data Analysis
Allende, H., Galbiati, J., Vallejos, R.: Robust image modelling on image processing. Pattern Recognition Letters 22, 1219–1231 (2001)
Bustos, O.H., Lucini, M.M., Frery, A.C.: M-estimators of roughness and scale for \(\mathcal{G}^{0}_{A}\)-modelled SAR imagery. EURASIP Journal on Applied Signal Processing 1, 105–114 (2002)
Cribari-Neto, F., Frery, A.C., Silva, M.F.: Improved estimation of clutter properties in speckled imagery. Computational Statistics and Data Analysis 40 (4), 801–824 (2002)
Frery, A.C., Müller, H.J., Yanasse, C.C.F., Sant’Anna, S.J.S.: A model for extremely heterogeneous clutter. IEEE Transactions on Geoscience and Remote Sensing 35(3), 648–659 (1997)
Hampel, F., Ronchetti, E., Rousseeuw, P., Stahel, W.: Robust Statistics. Wiley, New York (1986)
Mejail, M.E., Frery, A.C., Jacobo-Berlles, J., Bustos, O.H.: Approximation of distributions for SAR images: proposal, evaluation and practical consequences. Latin American Applied Research 31, 83–92 (2001)
Marazzi, A., Ruffieux, C.: Implementing M-estimators for the gamma distribution. In: Helmut, R. (ed.) Robust Statistics, Data Analysis and Computer Intensive Methods. Lecture Notes in Statistics, vol. 109, Springer, Berlin (1996)
Oliver, C., Quegan, S.: Understanding Synthetic Aperture Radar Images. Artech House, London (1998)
Rousseeuw, P.J., Verboven, S.: Robust estimation in very small samples. Computational Statistics and Data Analysis 40 (4), 741–758 (2002)
Tur, M., Chin, K.C., Goodman, J.W.: When is the speckle noise multiplicative? Applied Optics 21, 1157–1159 (1982)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Allende, H., Pizarro, L. (2003). Robust Estimation of Roughness Parameter in SAR Amplitude Images. In: Sanfeliu, A., Ruiz-Shulcloper, J. (eds) Progress in Pattern Recognition, Speech and Image Analysis. CIARP 2003. Lecture Notes in Computer Science, vol 2905. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24586-5_15
Download citation
DOI: https://doi.org/10.1007/978-3-540-24586-5_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20590-6
Online ISBN: 978-3-540-24586-5
eBook Packages: Springer Book Archive